fu a major oilfield in Saudi Arabia, detailed information on individual well performance is required for operating and planning purposes. Complete design data can only be obtainec through the addition of extensive facilities for periodic testing of all production in the field. Three different test pipeline configurations were considered. Variables included pipe diameter, pipeline length, oil production rate, gas/liquid ratio, and water cut. Lengths up to approximately 10 miles (16.1 km) were considered.Three types of analyses were undertaken. The first analysiS included steady state pressure losses in not only the horizontal segments but also the vertical risers and downcomers at the platforms. The second set of calculations involved transient simulations for both increasing and decreasing flow rates. Finally, slug length and period predictions for both normal and "severe" slugging were performed. Severe slugging occurs only at very low flow rates and negative pipeline inclination angles.Pressure drop calculations based on the Beggs & Brill correlation, with a rough pipe friction factor, showed that 10 in. (25.4 cm) pipelines were adequate for most cases. Transient simulation results indicated that, in general, less than two hours were required to reestablish steady state flow for both increasing and decreasing rates. Normally occurring slugs were found to vary in average length from about 200 to 600 ft (61 to 183m)with a maximum possible slug length of approximately 2500 ft (762 m). Liquid slug lengths for severe slugging varied from about 100 to 800 ft (30.5 to 244 ill depending on the pipeline length and riser height.

Summary A model is presented for predicting the pressure and temperature profiles for gas/liquid flow in wellbores stimulated with downhole heaters. The solution algorithm consists of coupling the momentum- and energy-balance equations for the wellbore fluids with the transient behavior in the surrounding rock. The heater is treated as a source term in the energy-balance equation. The model is capable of predicting wellbore thermal effects, such as reduction of fluid viscosity and increase of free gas. It can be used to simulate temperature maintenance during well shut-in periods to prevent pour-point problems and paraffin or hydrate formation. An existing well with production problems related to thermal effects was simulated by the model. Results indicate that wellbore temperatures can be controlled by downhole heaters. Also, under certain conditions, an increase in production was predicted with such an application. Introduction Many problems encountered in the production of oil and gas could be reduced by introduction of heat to increase the temperature of the fluids. A review of thermal stimulation methods was given by Farouq Ali, who included electrical or hot water heaters, gas bumers, limited in-situ combustion, and hot water or steam injection. Increasing the fluid temperature results in several advantages. Various reservoirs produce fluids that deposit semisolid asphaltic hydrocarbons. These solids can plug the flow path either in perforations or in the piping system. A similar phenomenon is the deposition of wax in tubing or flowlines, resulting in additional flow re-sistance. Thermal stimulation can reduce this deposition and increas well productivity. An increase in temperature will also result in a reduction in oil viscosity. This is important when highly viscous crudes are produced where frictional pressure losses can be large. Another positive result of beating is an increase in free gas, which in turn results in a lower pressure gradient in the system. Heating can also be used for temperature maintenance during well shut-in periods to prevent pour-point problems. Hydrate formation can also be prevented by thermal stimulation. Additional advantages are thermal fractures, clay dehydration, and removal of residual water. An investigation of the use of electrical heaters to assist in the production of viscous or waxy crudes has resulted in the development of a simulation model for well flow analysis. The model allows the simulation of the temperature profile in the flowing fluids, including the effect of the electric heater. Evaluation of the two-phase-flow pressure losses in the tubing and the effect of heating on the production rate are also considered. PVT properties of the fluids in the tubing were predicted with fluid property correlations, with emphasis on the variations in the liquid viscosity and the solution GOR with temperature. A significant reduction in the solution gas can occur with heating. This reduction will increase the free gas available, thereby making implementation of a gas-lift procedure possible. Mathematical Formulation Determination of the flowing pressure traverse in tubing for the simultaneous transport of liquid and gas phases requires a knowledge of the temperature variation with depth because the PVT properties depend strongly on pressure and temperature. When the temperature distribution is not known, coupling the heat-balance equation with the mechanical-energy equation to calculate the pressure and temperature changes simultaneously becomes necessary and requires a double iterative procedure. Brill and Beggs described the procedure. When the temperature distribution is known or can be determined explicitly, a single iterative procedure that involves a standard marching algorithm is used to evaluate the flowing pressure and temperature profiles. The following sections show the development of explicit equations for temperature prediction in the wellbore. Flowing-Temperature Determination. Two cases are analyzed in this study. The first case considers no heater in the wellbore. A solution for this configuration, developed by Ramey for injection wells, is modified here for producing wells. This solution is then extended in the second case for a wellbore with a heater. Without Heater. The wellbore heat-transmission process is governed by the following equation: (1) (2) The functionf(t) represents the resistance for radial conductive heat transfer in porous media. A discussion of f(t) is given in Appendix B. The boundary condition for a producing well can be written as (3) Note that D=O represents the bottom of the producing well in this equation. The geothermal temperature, represented as a linear function of depth, is given by (4) Solution of the partial-differential equation given by Eq. 1 with the boundary condition given by Eq. 3 yields (5) The solution assumes that the thermal properties of the earth and the wellbore fluids are independent of temperature. Also, heat transfer in the wellbore is assumed to be steady state, while heat flow to the surrounding rocks is described by the transient radial-heat-flow equation. The overall heat-transfer coefficient, Uo, incorporates the resistance to heat flow by the flowing fluids, tubing wall, fluid in the casing/tubing annulus, and casing wall. With Heater. A heat balance on an element of the two-phase flow mixture within the heater region yields (6) where S = rate of he-at supply per foot by the heater. The assumptions used in deriving Eq.1 were also applied in determining the heat balance represented by Eq. 6. The heater generally is not attached to the entire tubing string. Therefore, part of the tubing surface, usually near the bottom of the well, is always bare of the heater, while the remainder is always in contact with the heat source. Eq. 6 is valid only for fluid flow within the heated pipe section. An appropriate boundary condition for this situation is given by (7) In this case, D=O represents the location of the heater base. TFH(t), the temperature of the two-phase mixture at the entrance to the bottom section of the heater, is evaluated with Eq. 5. The geothermal temperature for this case is given by (8) SPEPE P. 309^

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The Artificial Neural Network (ANN) has been used as a predictive tool for the estimation of certain parameters of Subernarekha, an important river in the Jharkhand state in India. The network used two algorithms for this purpose and was sufficiently accurate in predicting the most economically heavy and time-consuming set of data. The Levenberg-Marquardt Backpropagation Network (trainlm) and the Resilient Backpropagation Network (trainrp) were the two algorithms used for the estimation of metallic species and physicochemical parameters of the river. The MAPE for metallic species were found to be 0.71 for cadmium, 0.182 for copper and 0.771 for chromium, while physicochemical parameters were 16.645 for alkalinity, 5.883 for dissolved oxygen (DO) and 23.28 for chemical oxygen demand (COD). Both the algorithms were used with different sets of hidden layers i.e., one for trainlm with 5 neurons and three for trainrp with 5, 4 and 5 neurons, and these were determined using the trial and error method. This method is not only economically favorable but time-adaptive as well, as it depends on the amount and span of data available and it can predict values to a very high degree of accuracy. This method can successfully be employed for the prediction of parameters of any river system with confidence.

The laws of conservation of mass and linear momentum were applied to a two-phase mixture to formulate a mathematical model which simulates isothermal, transient two-phase flow of gas and liquid in a pipeline. Liquid holdup and friction factors were incorporated via existing empirical correlations, and the black oil method was used to describe interphase mass transfer. Implicit finite difference analogues were derived for the nonlinear set of partial differential equations which constituted the basis of the model. The system of difference equations was solved using a sequential solution algorithm implementing a Newton-Raphson iterative procedure. The numerical model formulated was used to predict the performance of an existing wet gas pipeline to establish the validity of the model. Example simulation runs were used to provide insights into the nature of transient two-phase flow. Downloaded From: http://energyresources.asmedigitalcollection.asme.org/ on 02/19/2015 Terms of Use: http://asme.org/terms 206/ Vol. 108, SEPTEMBER 1986 Transactions of the ASME Downloaded From: http://energyresources.asmedigitalcollection.asme.org/ on 02/19/2015 Terms of Use: http://asme.org/terms