Given the large amount of by-passed oil and gas and low oil recovery factors in many reservoirs coupled with global energy demand projected to rise as high as almost 60%in the next 30 years particularly in Africa, it is quite evident that convectional petroleum engineering techniques are not adequate and efficient for reservoir characterization and production optimization. This challenging trend may be met only by revolutionary breakthroughs in energy science and technology. The industry thus requires stunning discoveries in underlying core science and engineering which can only be accomplished through integration and global approach. Breakthroughs in nanotechnology open up the possibility of enhancing and optimizing oil and gas production beyond the current available technology by introducing innovative concepts that are more efficient and environmentally friendly. The term "nanotechnology" covers processes associated with the creation and utilization of structures in the 1.0 nanometer (nm) to 100 nm range. Nanofabrication involves engineering at the atomic length scale. It no doubt, offers substantial economic and societal benefits than any technology and holds the promise of both incremental improvements of existing products and the potential for revolutionary changes that could transform entire industries. Nanotechnology is characterized to be a multidisciplinary field, making it inherently innovative and more precise than other technologies. In this paper, the greatest potential for innovative solutions in enhanced oil recovery (particularly water flooding) through the investigation and synthesis of water-based nano-particle will be discussed.

Chemicals are used in various stages of oil production such Drilling (Drilling fluids, cementing, completion and workover fluids), Production, Stimulation and Enhanced Oil Recovery. Many research studies have shown these oil field chemicals have toxic effects on the environment. The oilfield chemicals include various additives for drilling/cementing and work-over such as Fluid loss additives, rheology modifiers, Viscosifiers, Emulsifiers, Biocides, Surfactants, Packer fluid corrosion inhibitors. Toxicity tests are crucial for the assessment of the harmful effects of complex chemical mixtures, such as waste drilling mud, hydraulic fracturing fluid on aquatic environment. The objective of the study is to develop screening protocol to assess, evaluate, and manage the inherent risks. To achieve this, it is imperative to develop models, tools and an acceptable mechanism for screening, predicting and monitoring the application of oil field chemicals. In this paper, Adaptive Genetic Neuro-Fuzzy Inference System is developed to assess the toxicity of oilfield chemicals. Several toxicological studies have shown the evidence of toxicity of some oilfield chemicals to living organisms and their potentially negative side effects on environmental ecosystems for which relatively tedious animal testing methodologies are documented for their assessment. The description of this intelligent system is provided and has proven to have better classification and regression capability and ability to handle high dimensional features. This study adopts a novel evolutionary computing approach to search and obtain the optimal Neuro-Fuzzy parameters to enhance the prediction accuracy and generalization capability of the model. The system was applied to a dataset on Oil Field Chemicals toxicity and it was found that the genetic algorithm yields optimal parameters of Neuro-Fuzzy for the given datasets. The prediction and classification of Oil Field Chemicals (toxic or non-toxic) using this hybrid intelligent system is a work that requires an in-depth study and understanding of the various underlying principles of Neuro-Fuzzy inference system and Genetic Algorithm, which is commonly applied for classification and regression purposes. The developed model based on the fuzzy rules was trained with available data set. The unseen or new data is therefore either classified into appropriate class or have toxicity predicted using gaussian membership function chosen for this application. The motivation of this approach is that it is less cumbersome than the conventional computational modeling usually adopted for chemical classification and characterization. It also seeks to eradicate the existing animal testing that are hitherto very tedious and cumbersome.

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