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Prediction of oilfield scale formation using artificial neural network (ANN)
(SCIENCEDOMAIN International, 2016-07) Falode, O. A.; Udomboso, C.; Ebere, F.
Scale formation and deposition is a recurring problem in many oil producing fields leading to operational problems, problems in reservoirs, pumps, valves and topside facilities. Scale is described economically as a menace to an oil-field because its build-up clogs the flow lines and causes loss of millions of dollars yearly. The ability to predict the onset and amount of scale formation has been a major challenge in the oil industry. Previous models for predicting scale formation have focused mainly on thermodynamics and limited solubility data, and can predict only the potential or tendency to form scale. However, no studies have considered the influence of kinetic and transport factors. In this paper, a comprehensive and robust model incorporating other factors that have been ignored in past studies is developed using the technique of artificial neural network (ANN). Field data on two types of scale namely Barium and Calcium sulphate were obtained, processed, trained and tested with Artificial Neural Network. The model obtained was validated with actual data. Results show that at constant pressure, the neural network structure with optimum performance for BaSO(4) was ANN {1,2,1} with the lowest Mean Square Value (MSE) of 0.0025 and the highest correlation determination R(2) of 0.9966 while at constant temperature, it was ANN{1,1,1} with MSE of 0.0017 and R(2) of 0.9956. The neural network structure with optimum performance for CaSO4 precipitation kinetics with temperature and pressure was ANN{2,5,1} with MSE of 8.7745e-005 and R(2) of 0.8206 while at constant flow rate it was ANN{1,4,1} with MSE of 2.3007e-006 and R(2) of 0.9953. This gave a very close agreement with actual data in terms of prediction and performance. The results of this study therefore will greatly help to reduce the amount of risk incurred (such as NORM, etc.) due to the deposition and formation of scale in an oilfieldthe cost of stimulating an oil flow line and also improve the productivity of an oil well, hence, increase revenue to the oil industry.
SPATIAL AND TEMPORAL VARIATIONS OF PHYSICOCHEMICAL CHARACTERISTICS OF SURFACE WATER AND SEDIMENT OF OSUN RIVER IN SOUTHWESTERN NIGERIA
(2012-09) ABIDEMI, OLAYIWOLA, OLAJUMOKE
Osun River is important for domestic, recreational and other activities. It flows along a channel that may be polluted by inputs from industrial, agricultural and other anthropogenic activities thereby limiting its normal use for drinking, fishing, recreation and other purposes. Available literature on the river quality is limited in scope, frequency of sampling and duration of studies. Therefore, a study of the river and its tributaries was carried out to determine the spatial and temporal variations of physicochemical characteristics of its water and sediment. Surface water and sediments were sampled bimonthly from July 2006 to May 2008 at upstream and downstream points of the main river course and 31 tributaries. Sampling was by compositing at each point of 90 locations for surface water and 63 identified locations for sediment, where possible. Water samples were analysed for alkalinity, hardness, ammonia, anions, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), heavy metals and turbidity. Sediment samples were analysed for organic carbon, particle size and selected heavy metals using APHA methods. Location-based and overall data obtained were fitted into a time series model using a number cruncher statistical system, and applied to predict contaminant concentrations up to year 2018. The Pratti model was applied to determine locational pollution classes (Class 1-5) based on gross organic pollutants and ammonia. Statistical evaluation of data involved use of principal component analysis, analysis of variance and Student’s t-test at p = 0.05. The concentrations (mg/L) of alkalinity, hardness, ammonia, nitrate, phosphate and chloride were 93±130, 116±120, 4.2±6.6, 1.8±1.5, 0.15±0.23 and 54±110 respectively. Those of DO, BOD, COD, lead, copper, cadmium and zinc were 7.9±3.0, 6.9±7.5, 135±120, 0.003±0.004, 0.003±0.004, 0.002±0.003, 0.07±0.10 mg/L respectively and turbidity, 34±43 FTU. Values of parameters for upstream locations did not differ significantly from downstream points, indicating randomness of contaminant inputs. Turbidity, sulphate and DO were higher during the wet seasons while phosphate, nitrate and BOD were higher in the dry seasons. Metal levels correlated positively between water and sediment, with coefficients ranging between 0.75 for Cu and 0.99 for Co. Highest concentration factors in sediment were 233 (Pb) and 171 (Zn). Inter-element association in sediment was high only for Pb/Cu (r =+0.72). Two locations fitted into Class 4 (grossly polluted) of the Pratti scale, while thirty-one were Class 3 (slightly polluted) which was UNIVERSITY OF IBADAN LIBRARY iv indicative of pollution derived from mild industrial and agricultural impacts. Fifty-three locations were acceptable (Class 2), and four excellent (Class 1). Time series modelling fitted well for nitrate (R2= 0.79), phosphate (R2= 0.84) and BOD (R2= 0.71) data and gave their 2018 predicted values of 19.2, 18.1 and 21.9 mg/L respectively. Comparison with WHO guidelines indicated that 37.0% of sampling points for surface water were unfit for drinking mostly due to high turbidities, but suitable for irrigation. Metal levels in sediment were within international limits. Osun River and its tributaries have been adversely impacted upon by non-point pollutant inputs. Further deterioration in the near future was predicted, and heavy metal pollution is not yet a significant problem in the river basin. Keywords: Osun River, Gross organic pollution, Modelling, Spatial variation, Water quality. Word count: 500

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