PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.

The development of a photoacoustic Instrument for the monitoring of oil and other hydrocarbons in water.

The present methods for the detection of oil in discharge water are based either on chemical analysis of intermittent samples or bypass pipelines with instrumentation to detect either dissolved or dispersed hydrocarbons by a variety of optical techniques including absorption, scattering and fluorescence. However, test have shown that no single instruments entirely meets either present needs or satisfies the requirements of the future more stringent legislation which may limit total hydrocarbon content to 30 ppm or even less. Hence, in this paper, a detector is devised which can detect both dissolved and dispersed oil products, has a high immunity to scattering and can operate in-line and harsh environments with a detection sensitivity of a few ppm throughout a wide range of operations.