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Abstract Practical production optimization problems typically involve large, highly complex reservoir models, thousands of unknowns and many nonlinear constraints, which makes the numerical calculation of gradients for the optimization process impractical. This work explores a new algorithm for production optimization using optimal control theory. The approach is to use the underlying simulator as the forward model and its adjoint for the calculation of gradients. Direct coding of the adjoint model is, however, complex and time consuming, and the code is dependent on the forward model in the sense that it must be updated whenever the forward model is modified. We investigate an adjoint procedure that avoids these limitations. For a fully implicit forward model and specific forms of the cost function and nonlinear constraints, all information necessary for the adjoint run is calculated and stored during the forward run itself. The adjoint run then requires only the appropriate assembling of this information to calculate the gradients. This makes the adjoint code essentially independent of the forward model and also leads to enhanced efficiency, as no calculations are repeated. Further, we present an efficient approach for handling nonlinear constraints that also allows us to readily apply commercial constrained optimization packages. The forward model used in this work is the General Purpose Research Simulator (GPRS), a highly flexible compositional/black oil research simulator developed at Stanford University. Through two examples, we demonstrate that the linkage proposed here provides a practical strategy for optimal control within a general-purpose reservoir simulator. These examples illustrate production optimization with conventional wells as well as with smart wells, in which well segments can be controlled individually. Introduction Most of the existing major oilfields are already at a mature stage, and the number of new significant discoveries per year is decreasing [1]. In order to satisfy increasing worldwide demand for oil and gas, it is becoming increasingly important to produce existing fields as efficiently as possible, while simultaneously decreasing development and operating costs. Optimal control theory is one possible approach that can be deployed to address these difficult issues. The main benefit of the use of optimal control theory is its efficiency, which makes it suitable for application to real reservoirs simulated using large models, in contrast to many existing techniques. The above-mentioned problem is essentially an optimization problem, wherein the objective is to maximize or minimize some cost function J(u) such as net present value (NPV) of the reservoir, sweep efficiency, cumulative oil production, etc. Here, u is a set of controls including, for example, well rates and bottom hole pressures (BHP), which can be manipulated in order to achieve the optimum. In other words, u is anything that can be controlled. It should be understood that the optimization process results in control of future performance to maximize or minimize J(u), and thus the process of optimization cannot be performed on the real reservoir, but must be carried out on some approximate model. This approximate model is usually the simulation model of the reservoir. This simulation model is a dynamic system that relates the controls u to the cost function J(u). Consider for example the simple schematic of a reservoir shown in Fig. 1, where the cost function is cumulative oil production and the control is the injection rate. Changing the injection rate changes the dynamic states of the system (pressures, saturations), which changes the oil production rate of the producer, which in turn impacts the cost function. Thus, the controls u are related to J(u) through the dynamic system. The dynamic system can also be thought of as a set of constraints that determine the dynamic state of the system given a set of controls. Further, the controls u themselves may be subject to other constraints that dictate the feasible or admissible values of the controls, such as surface facility constraints or fracture pressure limits. It is these additional constraints that in many cases complicate the problem and the solution process.
- Europe (1.00)
- North America > United States > Texas (0.46)
- Europe > United Kingdom > North Sea > Central North Sea > Ness Formation (0.99)
- Europe > Norway > North Sea > Tarbert Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Ness Formation (0.99)
- Europe > Germany > North Sea > Tarbert Formation (0.99)
Abstract Drift-flux models represent multiphase flow in wellbores or pipes in terms of a number of empirically determined parameters. Because of the lack of data for two and three-phase flow in large-diameter inclined pipes, existing parameters are commonly based on small-diameter pipe experiments, which can lead to significant errors when the models are applied to wellbore flows. In this work, we use recent large-diameter experimental data for the determination of drift-flux parameters for oil-water-gas flow. The parameters are computed through application of an optimization procedure. It is shown that gas holdup in three-phase systems can be estimated using a two-phase flow model by viewing the system as an effective gas-liquid system, with oil and water comprising the ‘liquid’ phase. This approach is, however, generally inaccurate for the determination of oil and water holdups, in which case the effect of gas must be taken into account. Specifically, for pipe inclinations away from horizontal, even small amounts of gas can act to eliminate the slip between oil and water. As the pipe deviation approaches horizontal, however, oil-water slip persists, even in the presence of gas. We develop and apply a unified two and three-phase flow model to capture this gas effect. The new model is shown to provide much more accurate predictions for oil and water holdups in three-phase systems than were achievable with previous models. Introduction Drift-flux modeling techniques are commonly used to represent two and three-phase flow in pipes and wellbores. These models are well-suited for use in reservoir simulators because they are relatively simple, continuous, and differentiable. Drift-flux models require a number of empirical parameters. Most of the parameters used in current simulators were determined from experiments in small diameter (5 cm or less) pipes and may therefore not be appropriate for large diameter wellbores. In recent work, we described a new research program, which includes experimental and modeling components, aimed at the determination of drift-flux parameters for large-diameter deviated wells. The experimental work entailed water-gas, oil-water and oil-water-gas flows in a 15 cm diameter, 11 m long plexiglass pipe at 8 deviations ranging from vertical to 2 degrees downward. Unique steady-state holdup data were measured using several different experimental techniques. Our previous work provided optimized drift-flux parameters for two-phase water-gas and oil-water flows. Here we extend the analysis to three-phase flows. Even though the simultaneous flow of oil, water and gas is very common in wellbores and pipelines, systems of this type are not fully understood. Most of the studies to date have focused on horizontal or near-horizontal flows. Açikgöz et al. classified the observed 10 flow patterns in a horizontal 1.9 cm pipe into two categories: oil-based and water-based flows, depending on which phase is dominant in the liquid. No three-layer stratified flows were observed in their experiments. Following the work of Açikgöz et al., Lahey et al. used the same experimental facility to collect three-phase holdup data. These data were then used to determine the drift-flux parameters C0 and Vd for each of the 10 flow patterns. They found that the values of C0 and Vd could be significantly different from one flow pattern to another and, as expected, the drift velocities were quite small compared with those in vertical flows. It is, however, clear that general simulation models are still lacking for three-phase stratified flows. The state of three-phase flow modeling is even less developed for deviated pipes and wells. Because comprehensive three-phase flow models are lacking, one treatment for three-phase flow is to combine oil and water into a single ‘liquid’ phase and to then model the system as a two-phase liquid-gas flow. In this treatment, the slip between oil and water is ignored and a homogeneous mixture is assumed for the liquid phase. Some studies indicate that this simple treatment can lead to significant errors in phase holdup predictions, while other observations suggest that this approach is valid. In this work, we will use our experimental data and model to clearly quantify the range of validity of this approach.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Multiphase flow (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Drift-Flux Modeling of Multiphase Flow in Wellbores
Shi, H. (Stanford University) | Holmes, J.A. (Schlumberger GeoQuest) | Durlofsky, L.J. (ChevronTexaco EPTC) | Aziz, K. (Stanford University) | Diaz, L.R. (Stanford University) | Alkaya, B. (Stanford University) | Oddie, G. (Schlumberger Cambridge Research)
Abstract Drift-flux modeling techniques are commonly used to represent two and three-phase flow in pipes and wellbores. Unlike mechanistic models, drift-flux models are continuous, differentiable and relatively fast to compute, so they are well suited for use in wellbore flow models within reservoir simulators. Drift-flux models require a number of empirical parameters. Most of the parameters used in current simulators were determined from experiments in small diameter (2 inch or less) pipes. These parameters may not be directly applicable to flow in wellbores or surface facilities, however, as the flow mechanisms in small pipes can differ qualitatively from those in large pipes. In order to evaluate and extend current drift-flux models, an extensive experimental program was initiated. The experiments entailed measurement of water-gas, oil-water and oil-water-gas flows in a 15 cm diameter, 11 m long plexiglass pipe at 8 deviations ranging from vertical to slightly downward. In this paper, these experimental data are used to determine drift-flux parameters for steady state two-phase flows of water-gas and oil-water in large-diameter pipes at inclinations ranging from vertical to near-horizontal. The parameters are determined using an optimization technique that minimizes the difference between experimental and model predictions for holdup. It is shown that the optimized parameters provide considerably better agreement with the experimental data than do the existing default parameters. Introduction Multiphase flow effects in wellbores and pipes can have a strong impact on the performance of reservoirs and surface facilities. In the case of horizontal or multilateral wells, for example, pressure losses in the well can lead to a loss of production at the toe or overproduction at the heel. In order to model and thereby optimize the performance of wells or reservoirs coupled to surface facilities, accurate multiphase pipeflow models must be incorporated into reservoir simulators. Within the context of petroleum engineering, the three types of pipeflow models most commonly used are empirical correlations, homogeneous models and mechanistic models. Empirical correlations are based on the curve fitting of experimental data and their applicability is generally limited to the range of variables explored in the experiments. These correlations can be either specific for each flow pattern or can be flow pattern independent. Homogeneous models assume that the fluid properties can be represented by mixture properties and single-phase flow techniques can be applied to the mixture. These models can also allow slip between the phases and this requires a number of empirical parameters. Homogeneous models with slip are called drift-flux models. Mechanistic models are in general the most accurate as they introduce models based on the detailed physics of each of the different flow patterns. From a reservoir simulation perspective, however, mechanistic models can cause difficulties because they may display discontinuities in pressure drop and holdup at some flow pattern transitions. Such discontinuities can give rise to convergence problems within the simulator. One approach to avoid these convergence issues is to introduce smoothing at transitions. An alternative approach is to apply a homogeneous pipeflow model. The drift-flux model is in fact a simple mechanistic model for intermittent flows, and it is used within general mechanistic models when the flow pattern is predicted to be bubble or slug.
- Well Drilling > Drilling Operations > Directional drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Multiphase flow (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Abstract Mechanistic models for multiphase flow calculations can improve our ability to predict pressure drop and holdup in pipes, especially in situations that cannot easily be modeled in a laboratory and for which reliable empirical correlations are not available. In this paper, a new mechanistic model, applicable to all pipe geometries and fluid properties, is presented. New empirical correlations are proposed for liquid/wall and liquid/gas interfacial friction in stratified flow, for the liquid fraction entrained and the interfacial friction in annular-mist flow, and for the distribution coefficient used in the determination of holdup in intermittent flow. Introduction Empirical models often prove inadequate in that they are limited by the range of data on which they were based and, generally, cannot be used with confidence in all types of fluids and conditions encountered in oil and gas fields. Furthermore, many such models exhibit large discontinuities at the flow pattern transitions and this can lead to convergence problems when these models are used for the simultaneous simulation of petroleum reservoirs and associated production facilities. Mechanistic models, on the other hand, are based on fundamental laws and thus can offer more accurate modeling of the geometric and fluid property variations. All of the models presented in the literature are either incomplete, in that they only consider flow pattern determination, or are limited in their applicability to only some pipe inclinations. A preliminary versionof the model proposed here that overcomes these limitations was presented in 1996. For most of the flow patterns observed, one or more empirical closure relationships are required even when a mechanistic approach is used. Where correlations available in the literature are inadequate for use in such models, new correlations must be developed. In order to be able to achieve this, access to reliable experimental data is important. A large amount of experimental data has been collected through the use of a Multiphase Flow Database developed at Stanford University. The database presently contains over 20,000 laboratory measurements and approximately 1,800 measurements from actual wells. Based on subsets of these data, the previously proposed model included a detailed investigation of the annular mist flow regime and new correlations for the liquid fraction entrained and for interfacial friction. This model has since been refined based on additional investigations of the stratified and intermittent flow regimes, and is the subject of this paper. Flow Pattern Determination The procedure for flow pattern determination begins with the assumption that a particular flow pattern exists and is followed by an examination of various criteria that establish the stability of the flow regime. If the regime is shown to be unstable, a new flow pattern is assumed and the procedure is repeated. Figure 1 shows flow pattern transitions based on the superficial velocities of the phases where the stability criteria (transition boundaries S1, S2, etc.) considered in this model are sketched. The procedure for flow pattern determination is illustrated in Figure 3, where it is seen that the examination of the dispersed bubble flow regime is the first to be considered.
Abstract Non-conventional wells that include horizontal, highly deviated, and multilateral wells-often called designer wells-are becoming more and more common. Both analytical and numerical tools have been developed and continue to be developed for predicting their performance. Unfortunately, predictions made using these tools rarely match actual performance, except in cases where sufficient production data are available for history matching and the model used for making predictions is selected carefully. Even then the predictions are generally good only for a limited time. In this paper we explore reasons for our inability to make accurate predictions. We consider a case where a vertical well has been drilled and cored. Then, we generate twenty consistent geostatistical descriptions of permeability and porosity that are all constrained to the hard data obtained from the vertical well. Simulations with these realizations show large differences in production rate, WOR and GOR predictions as a result of variations in reservoir properties. It is also shown that the effect of well index (WI) on simulation results is large. Furthermore, for the example considered, analytical models for critical rate and productivity calculations were found to be of limited practical use. Introduction In a recent talk at Stanford University, Edward Teller was asked what had changed in science over the past 60 years or so since he immigrated to the U.S. He responded by saying that "then we believed that everything could be predicted, now we know that future can only be predicted in a probabilistic sense." While Teller was talking about physics, his remarks are equally valid for other areas of science and engineering. Yogi Berra understood this problem well when he said: "predictions are difficult to make, especially with regard to the future." In most cases the prediction of the aggregate effect of random events is sufficient for engineering purposes. For example pressure drop caused by the flow of gas in a pipeline is a consequence of the motion of individual molecules. While we cannot-nor do we want to-predict the behaviour of individual molecules, we can predict everything of practical significance: pressure and temperature distribution, average velocity at every location, etc. In order to make such predictions we have to be able to describe our system, and its initial and boundary conditions. In the case of steady-state flow in a pipeline we must specify:Pipe diameter, length, profile, and roughness; Initial state of fluid in the pipe; and Interaction with the boundaries. Even for this simple problem there are uncertainties. Heat transfer from the pipe to the surroundings will depend on the material in which the pipe is buried and the ambient conditions, which are never known precisely. Wells drilled in petroleum reservoirs interact in a complex way with the reservoir. In order to predict their behaviour we must be able to model multiphase flow in the well and the reservoir. In this problem there are many sources of uncertainties, some of these are explored in this paper. The most serious of these is the limited data about the reservoir itself.
Abstract Mechanistic models for multiphase flow calculations can improve our ability to predict pressure drop and holdup in pipes especially in situations that cannot easily be modeled in a laboratory and for which reliable empirical correlations are not available. In this paper, a new mechanistic model, applicable to all pipe geometries and fluid properties is presented. New empirical correlations are proposed for liquid/ wall and liquid/gas interfacial friction in stratified flow, for the liquid fraction entrained and the interfacial friction in annular-mist flow, and for the distribution coefficient used in the determination of holdup in intermittent flow. Introduction Empirical models often prove inadequate in that they are limited by the range of data on which they were based and, generally, cannot be used with confidence in all types of fluids and conditions encountered in oil and gas fields. Furthermore, many such models exhibit large discontinuities at the flow pattern transitions and this can lead to convergence problems when these models are used for the simultaneous simulation of petroleum reservoirs and associated production facilities. Mechanistic models, on the other hand, are based on fundamental laws and thus can offer more accurate modeling of the geometric and fluid property variations. All of the models presented in the literature are either incomplete , in that they only consider flow pattern determination, or are limited in their applicability to only some pipe inclinations. A preliminary version of the model proposed here that overcomes these limitations was presented in 1996. For most of the flow patterns observed, one or more empirical closure relationships are required even when a mechanistic approach is used. Where correlations available in the literature are inadequate for use in such models, new correlations must be developed. In order to be able to achieve this, access to reliable experimental data is important. A large amount of experimental data has been collected through the use of a Multiphase Flow Database developed at Stanford University. The database presently contains over 20,000 laboratory measurements and approximately 1800 measurements from actual wells. Based on subsets of these data, the previously proposed model included a detailed investigation of the annular-mist flow regime and new correlations for the liquid traction entrained and for interfacial friction. This model has since been refined based on additional investigations of the stratified and intermittent flow regimes, and is the subject of this paper. Flow Pattern Determination The procedure for flow pattern determination begins with the assumption that a particular flow pattern exists and is followed by an examination of various criteria that establish the stability of the flow regime. If the regime is shown to be unstable, a new flow pattern is assumed and the procedure is repeated. Figure 1 shows flow pattern transitions based on the superficial velocities of the phases where the stability criteria considered in this model are sketched. The procedure for flow pattern determination is illustrated in Figure 3, where it is seen that the examination of the dispersed bubble flow regime is the first to be considered.
Abstract The main purpose of this paper is to show the importance of chokes in thesimulation of surface facilities and reservoirs as integrated systems. In order to predict producing rates accurately, it is necessary not only to model the fluid flow in the reservoir but also to couple the gathering system to the reservoir. The interaction between these two parts can have a great influence in the performance of the whole system and this interaction is greatly affected by the use of chokes in surface facilities. Existing choke models are evaluated and the effect of placing chokes in the surface model is studied and some results are presented. Techniques for simultaneous simulation of reservoir, well and surface facilities will be presented in a future paper. Introduction The main reason for having a choke in the system is to control flow rate and, consequently, produce at the most efficient rate, prevent gas and water conning, avoid formation damage, prevent excessive sand production and protect equipment from pressure fluctuations. The choke is a very important unit of production facilities because a significant part of the total pressure drop can occur in the choke. A choke can decouple the reservoir or a particular reservoir domain from the production facilities, thus changing the characteristics of the entire system. Because surface chokes have the function of controlling rates, many authors have stated that they should be sized to operate under critical flow. Critical flow occurs when the velocity of the fluid reaches the sonic velocity. At this point, variations in the downstream side of the choke cannot be felt upstream and, therefore, as shown in Fig. 1, there is no rate variation, even if the downstream pressure decreases. This rate is called the critical rate and it is function only of the upstream parameters.
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Facilities Design, Construction and Operation > Processing Systems and Design (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (0.71)
- Well Completion > Completion Selection and Design > Completion equipment (0.50)
SPE Members Abstract In 1968, Vogel used a computer program to predict inflow performance relationships (IPR) of a well producing from a wide variety of solution-gas drive reservoirs. In dimensionless form, these curves relating flowing, bottom-hole pressures to oil production rates were found to share a common characteristic shape which was correlated by a parabola. Because Vogel studied cases of differing rock and PVT properties, his work became well accepted in the industry. Most of the papers published on inflow performance deal with vertical wells and should not be applied to performance deal with vertical wells and should not be applied to horizontal wells without verification. There are no analytical models available at present to compute IPRs from two phase flow theory. In such a case, it is necessary to use numerical simulation. Unfortunately, most commercial black oil simulators do not include the feature necessary for predicting IPRs of horizontal wells. predicting IPRs of horizontal wells. In this paper, two commercial simulators are utilized to develop IPRs for horizontal wells producing from solution-gas drive reservoirs. The development parallels the work of Vogel for vertical wells. First, a base case is considered with typical fluid, rock and reservoir properties. Then, variations from the base case are investigated. Changes from the base case, -i fluid properties included variations in relative permeability and PVT properties. Changes in reservoir properties included variations in drainage area, pay thickness, and absolute permeability. Changes in well properties included variations in skin, well location, and well length with properties included variations in skin, well location, and well length with respect to reservoir boundaries. The resulting IPRs were made dimensionless in order to compare their curvature, or the rate of change of oil production rate with flowing bottom-hole pressure. These curves were found to be sensitive to the stage of reservoir depletion. However, they were not affected significantly by changes in the fluid reservoir, or well properties. properties. An attempt was made to develop a simple correlation to represent numerical results. Both Vogel's and Fetkovich's equations were tried, but neither of them fully reproduced the characteristic shape of the dimensionless IPRs. A new two-parameter equation, that results from combining the two previous equations was found to provide an adequate correlation. Introduction A step in the analysis of the deliverability of a well is to estimate the production rate for any given flowing bottom-hole pressure. In single phase production rate for any given flowing bottom-hole pressure. In single phase flow of oil, inflow into a well is usually directly proportional to the pressure differential between the reservoir and the wellbore. But in two pressure differential between the reservoir and the wellbore. But in two phase flow of oil and gas, this relationship between the production rate phase flow of oil and gas, this relationship between the production rate ana the wellbore pressure is no longer linear. An equation that describes this relationship is known as the inflow performance relationship of a well (IPR). Vogel has proposed an empirical IPR equation that has been used in the industry successfully. Because flow into a horizontal well is different from the flow into a vertical well, IPR equations developed for vertical wells should not be applied to horizontal wells without verification. Since the analytical calculations necessary to compute IPRs from two phase flow theory axe tedious, numerical simulation is used. Most commercial black oil simulators do not include the feature of making complete IPR predictions for a reservoir. predictions for a reservoir. In this paper, available commercial simulators are nevertheless utilized to generate IPRs of a horizontal well producing from a solution-gas drive reservoir. First, IPRs are generated for a base case reservoir. Then, variations of the base case are examined. These variations cover a wide range of fluid, reservoir and well characteristics. Fluid characteristics include crude oil PVT properties and relative permeability data. Reservoir characteristics include absolute permeability data. Reservoir characteristics include absolute permeability, drainage area, and formation thickness. Well characteristics permeability, drainage area, and formation thickness. Well characteristics include skin, location, and length with respect to the reservoir dimensions. P. 551
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 054 > Block 31/6 > Troll Field > Sognefjord Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 054 > Block 31/6 > Troll Field > Heather Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > North Viking Graben > PL 054 > Block 31/6 > Troll Field > Fensfjord Formation (0.99)
- (9 more...)
Gas-Liquid Flow In Upwards Inclined Pipe With Zero Net Liquid Production
Gregory, G.A. (Department of Chemical & Petroleum Engineering The University of Calgary) | Aziz, K. (Department of Chemical & Petroleum Engineering The University of Calgary) | Nicholson, M.K. (Department of Chemical & Petroleum Engineering The University of Calgary)
ABSTRACT Flow pattern, holdup and pressure drop data are reported and discussed for gas flow experiments in 25.8 and 51.2 mm I.D. pipes inclined upwards at 1°, 5° and 9.2°, While no liquid flows through the system, a certain amount of liquid circulates within the test section giving it many of the characteristics of two phase pipelines. There are many practical situations where liquid may be introduced, due to operational problems, to a system designed for single phase gas. The amount of liquid retained in a pipe depends upon its angle of inclination and the gas rate. Relatively high gas rates are required to blowout all of the liquid. Although no generalized correlations are proposed in this paper, the data reported clearly demonstrates the adverse effects of retained liquid on pressure loss in gas pipelines. INTRODUCTION The objective of this study was to examine the characteristics of gas flow in an upward inclined pipe in which liquid is present, but where there is no net flow of liquid. This situation can occur in a pipeline which has been shutdown and allowed to cool to the point where liquid drops out. The condensed liquid will collect at the low points in the line and the above conditions will occur during a subsequent start-up. The situation may also be approximated by a pipeline in which there is an actual two phase flow, but with a very low liquid-to-gas ratio. At low to medium gas velocities, liquid may accumulate at low points over a period of time in significant quantities. Thus while the actual net flow of liquid could be very small, local liquid-to-gas ratios could be quite large. Another case in which this could occur is where a slug of liquid is introduced into the pipeline as a result of a "kick" from an otherwise dry gas well following a stimulation treatment (e.g. acidizing, fracturing). It is only necessary that the gas velocity be sufficiently low that the liquids are not completely swept through the pipe. This paper describes the results of an experimental study of this condition which has been carried out at the University of Calgary. Results are presented and discussed for flow pattern observations, the equilibrium liquid holdup and pressure drop for various gas rates and angles of pipe Inclination. EXPERIMENTAL EQUIPMENT AND PROCEDURE All flow tests reported herein were carried out in 24.4 m long (80 ft) test sections mounted on the inclinable trestle of the university of Calgary multiphase flow loop. This trestle can be tilted at angles of up to + 9.2° from the horizontal. Air was used as the gas phase; the liquid phase consisted of a 35°API refined oil. The viscosity of the liquid phase was around 6 mPa.s (6 centipose) at the usual flowing temperature of about 22°C. Tests were performed at three inclination angles, +1°, +5 °, and +9.2° for two pipe diameters, 25.8 (1 in) and 51.2 (2 in) mm I. D.
- Research Report > New Finding (0.50)
- Research Report > Experimental Study (0.34)
Abstract Pressure drops in oil-well tubing, calculated by five well-known models are compared with actual field data covering a wide range of conditions. A detailed explanation is provided for the observed result that significantly higher accuracy is generally obtained when calculations are based on known bottom-hole conditions rather than known wellhead conditions. It is demonstrated that greater accuracy may be obtained by using measured bubble-point data than by using calculated values. The results of this study suggest that either the Aziz, Govier and Fogarasi or the Orkiszewski model should generally be used in preference to other methods for calculating pressure drops in oil-well tubing. Introduction A reliable estimate of the pressure drop in well tubing is essential for the solution of a number of important production engineering and reservoir analysis problems. For example, inflow performance calculations require a knowledge of pressure drop in the producing string as a function of flow rate. Many different models and correlations have appeared in the literature for this purpose. In addition, there are a number of correlations available for estimating the fluid properties required for these calculations when experimental data are not available. In recent years, computer programs containing many of these models and fluid property correlations have been developed and are readily available to the engineer. Unfortunately, however, the choice of the appropriate method for a particular problem is seldom obvious. Thus, some guidelines are required to help resolve the following questions:Which multiphase flow calculation method is the most reliable for a given system? What influence would errors in the estimation of fluid properties have on the pressure-drop calculations? In this paper, we attempt to answer these two questions for typical oil wells, where gas-oil flow occurs over at least part of the producing string. The resolution of the same two questions for gas-condensate wells requires a somewhat different treatment, and will be considered in a separate paper. Effect of Calculation Direction The total pressure drop over a differential length of the tubing may be separated into three components: (Equation Available In Full Paper) The first term,, is the contribution to the total pressure change due to the hydrostatic-head effects. It is proportional to the density of the fluid mixture inside the pipe under flowing conditions (in-situ density). The second term, aPr, includes all frictional effects, and it is proportional, among other factors, to the fluid velocity and viscosity. Finally, the third term, ΔPKE, results from velocity changes (acceleration) caused by the expansion of the fluid with decreasing pressure. In oil wells, the fluid properties are such that the proper engineering design usually requires that the flow pattern be bubble or slug (see reference 1 for a description of various flow patterns). In some situations, the gas/oil ratio (GOR) may be high enough to cause froth flow, but this situation is more common in condensate wells.
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (0.91)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Multiphase flow (0.73)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (0.57)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Gas-condensate reservoirs (0.56)