An Experimental Comparison Between a Recombined Hydrocarbon-Water Fluid and a Model Fluid System in Three-Phase Pipe Flow

Utvik, Ole Harald; Rinde, Trygve; Valle, Arne

doi:10.1115/1.1410365

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…Norsk Hydro ASA have through the last 15 years developed a multiphase laboratory with a number of test facilities for the studies of multiphase flow. Common for these facilities has been the use of realistic fluid systems at elevated pressures, which has proven to be of great significance compared to atmospheric systems using inert fluids [1].…”

Section: Introductionmentioning

confidence: 99%

Dual energy gamma tomography system for high pressure multiphase flow

Frøystein¹,

Kvandal²,

Aakre³

2005

Flow Measurement and Instrumentation

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Section: Introductionmentioning

confidence: 99%

Dual energy gamma tomography system for high pressure multiphase flow

Frøystein¹,

Kvandal²,

Aakre³

2005

Flow Measurement and Instrumentation

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“…Multiphase flow in the reservoir and through flow lines, including gas-lift performance curves, flow through restrictions, and inflow from reservoir into well, require empirical closure relations (for recent discussion of such empirical relations for flow lines, see for instance [10], [11], for flow through restrictions see [12], for inflow relations see [9], and references therein). Empirical relationships can be fitted against laboratory experiments, but experiments can be costly and small deviations between laboratory model and field can produce large differences in observed flow [13]. Even the most carefully constructed production model will require some fitting against production data to reflect the influence of un-modeled disturbances and structural uncertainty, for instance skin effects near the well, erosion of chokes or the build up of wax or hydrates in flow lines.…”

Section: Introductionmentioning

confidence: 99%

Production Optimization: System Identification and Uncertainty Estimation

2008

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1 Abstract Real-time optimization of oil and gas production requires a production model, which must be fitted to data for accuracy. A certain amount of uncertainty must typically be expected in production models fitted to data due to the limited information content in data. It is usually not acceptable to introduce additional excitation at will to reduce this uncertainty due to the costs and risks involved. The contribution of this paper is twofold. Firstly, this paper discusses estimation of uncertainty in production optimization resulting from fitting models to production data with low information content, a concept that has previously mainly been applied in reservoir management. Secondly, this paper illustrates how system identification can be used to find production models which can be solved with little computational effort and which are designed to be easily fitted to production data. The method is demonstrated on a synthetic example before being applied to a case study of a North Sea oil and gas field. In offshore oil and gas production, the suggested method is expected to have applications in the development of structured approaches to uncertainty handling, for instance excitation planning and real-time optimization under uncertainty. 2 Introduction Production in the context of offshore oil and gas fields, can be considered the total output of production wells, a mass flow of components including hydrocarbons, in addition to water, CO2, H2S, sand and possibly other components. Hydrocarbon production is for simplicity often lumped into oil and gas. Production travels as multiphase flow from wells through flow lines to a processing facility for separation, illustrated in Figure 1. Water and gas injection is used for optimizing hydrocarbon recovery of reservoirs. Gas lift can increase production to a certain extent by increasing the pressure difference between reservoir and well inlet. Multiphase flow rates are hard to measure. Measurements of total produced single phase oil and gas rates are usually available, and estimates of total water rates can often by found by adding different measured water rates after separation. To determine the rates of oil, gas and water produced from individual wells, the production of a single well is usually routed to a dedicated test separator where the rate of each separated component is measured. In single-rate well tests rates are only measured for the current setpoint, while rates are measured for several setpoints in multi-rate well tests. The total amount of oil, gas and water which can be separated and processed is constrained by the capacity of facilities, these capacities are themselves uncertain. Normally production is at setpoints where some of these capacities are at their perceived constraints, therefore a multi-rate well test cannot be performed without simultaneously reducing production at some other well, which may cause lost production and a cost. There is also a risk that changes in setpoints during testing may cause some part of the facilities to exceed the limits of safe operation, which may force an expensive shutdown and re-start of production. Well tests are only performed when a need for tests has been identified due to the costs and risks involved. Well tests are a form of planned excitation, some planned variation in one or more decision variables designed to reveal information on production through measurements. Production is constrained by several factors including, on the field level, the capacity of the facilities to separate components of production and the capacity of facilities to compress lift gas. The production of groups of wells may travel through shared flow lines or inlet separators which have a limited liquid handling capacity. The production of individual wells may be constrained due to slugging, other flow assurance issues or due to reservoir management constraints. In the context of oil and gas producing systems, real-time optimization has been defined as a process of measure-calculate-control cycles at a frequency which maintains the system's optimal operating conditions within the time-constant constraints of the system [1]. It has been suggested that real-time optimization could be divided into subproblems on different time scales to limit complexity, and to consider separately reservoir management, optimization of injection and reservoir drainage on the time scales of months and years, and production optimization, maximization of value from the daily production of reservoirs [1]. Reservoir management typically specifies constraints on production optimization to link these problems. The aim of production optimization is to determine setpoints for a set of chosen decision variables which are optimal by some criterion. These setpoints are implemented by altering the settings of production equipment. Decision variables may be any measured or computed variables associated with production which are influenced by changes in settings, but the number of decision variables is limited by the number of settings. We may for instance determine the settings of a gas lift chke by deciding a target lift gas rate, a target annulus pressure or a target gas lift choke settings and possibly by routing settings.

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“…The model oil-water flow structure can be recorded by a high-speed camera. By controlling the test temperature, surfactants and other additives, we could ensure the model oils have similar viscosity and interfacial tensions as the crude oils [9].…”

Section: Introductionmentioning

confidence: 99%

Rheological and Emulsification Behavior of Xinjiang Heavy Oil and Model Oils

Jing¹,

Tan²,

Hu³

et al. 2016

TOEFJ

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Abstract:Transparent model oils are commonly used to study the flow patterns and pressure gradient of crude oil-water flow in gathering pipes. However, there are many differences between the model oil and crude oils. The existing literatures focus on the flow pattern transition and pressure gradient calculation of model oils. This paper compares two most commonly used model oils (white mineral oil and silicon oil) with Xinjiang crude oil from the perspectives of rheological properties, oil-water interfacial tensions, emulsion photomicrographs and demulsification process. It indicates that both the white mineral oil and the crude oils are pseudo plastic fluids, while silicon oil is Newtonian fluid. The viscosity-temperature relationship of white mineral oil is similar to that of the diluted crude oil, while the silicon oil presents a less viscosity gradient with the increasing temperature. The oil-water interfacial tension can be used to evaluate the oil dispersing ability in the water phase, but not to evaluate the emulsion stability. According to the Turbiscan lab and the stability test, the model oil emulsion is less stable than that of crude oil, and easier to present water separation.

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An Experimental Comparison Between a Recombined Hydrocarbon-Water Fluid and a Model Fluid System in Three-Phase Pipe Flow

Cited by 10 publications

References 6 publications

Dual energy gamma tomography system for high pressure multiphase flow

Dual energy gamma tomography system for high pressure multiphase flow

Production Optimization: System Identification and Uncertainty Estimation

Rheological and Emulsification Behavior of Xinjiang Heavy Oil and Model Oils

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