The uncontrolled urban growth in cities comes with unattending alteration in the urban environmental system. This alteration as land use types change is most responsible for some of the problems witnessed in urban town such as the variation in land surface temperatures over time. This study used Remote sensing and GIS techniques to identify, mark and measure the extent of the various land uses from the Landsat TM image of 1995 and Landsat ETM+ image of 2006. The study revealed that the spatial and temporal changes in the land uses have greatly influenced the increase in the land surface temperature of each of the identified land uses. As vacant land and built-up area increased by 3.28 ha/yr and 78.34 ha/yr so did their land surface temperature increased by 0.083 o C/yr. While vegetation and water and wetland vegetation decreased, their respective land surface temperature increased by 0.075 o C/yr and 0.083 o C/yr. This increase in land surface temperature of the study area within the period of study suggest that the rise in temperature of the various land uses may encourage environmental problems associated with local climate change as heat waves and mosquito infestations which can cause human discomfort as its been witnessed today in Anyigba. Proper checks on the development and conversion of land uses, urban forestry and adequate planning if employed may help in managing this occurence from agravating other environmental problems associated with land uses change and climate change.

Integration among geophysics, geology, reservoir engineering, geochemists, geomechanics and management is truly essential, but needs some specific approaches and methodologies for developing and calibrating a study model capable of dealing with all and each of these aspects. The ability for a multitask project team to easily search, modify, visualize and/or analyze a multidisciplinary study results in a quick, responsive and easily comprehendible manner is still a problem of the petroleum industry. In this work, various modeling workflows were examined so as to highlight unavoidable interdependencies between these multidisciplinary specialists in the process of oil and/or gas reservoir studies. The traditional multidisciplinary working methods which were hitherto available are examined and some lapses identified. An optimized integrated study approach was further proposed. The optimized integrated approach is expected to have tremendous advantages in terms of improving the quality as well as flexibility of oil and gas reservoir studies, a working time reduction, and is expected to serve as a single final approach that can be adapted or used to tackle reservoir study problems.

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Deterministic rock physics models were applied in a shale-sand environment located in the West African lower Congo basin, with the aim of estimating total porosity and clay content from P-wave acoustic impedance. Assuming that the only minerals within the target reservoir are quartz and clay, Han et al. model was used to determine the clay content which is referred herein as model-based C, while Krief et al. model was applied to solve the P-wave impedance for total porosity and clay content. The latter operation is a challenging task because of the nature of the actual rock physics equation that relates the known acoustic impedance to three unknown reservoir properties. This inherent difficulty is circumvented by making use of an additional linear equation, which is derived from the petrophysical link between porosity and clay content. To achieve this goal, firstly, a rock physics model was established, and then the reservoir was delineated through a combination of P-wave impedance and Poisson's ratio. In the reservoir, total porosity and clay content were inverted based on P-wave impedance by applying the rock physics model of Krief et al. that related P-wave impedance to total porosity and clay content, alongside the established petrophysical link between the two reservoir properties. The result was found to be consistent on the well log scale. Uniquely, a good match was obtained when the methodology was repeated on the real seismic data.

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