Flowmeters are used to measure liquid/gas products. Turbine flowmeters are an effective means of accurate measurement of liquid/gas products in many industries. Because of the turbine meter's versatility and flexibility in product metering applications, it is one of the most widely used technologies in flow measurement. Turbine meters were invented in the 18th century by Reinhard Woltman, and at that time were used for water-flow measurement. In 1970, the API recognized the turbine meter in MPMS Chap. 5 Sec. 3, "Measurement of Liquid Hydrocarbons by Turbine Meters."
Gas turbines range in size from microturbines at 50 hp (37.3 kW) to large industrial turbines of 250,000 hp (190 kW). This page focuses on the gas turbine engine, the differences between types of turbines, and items to consider when they are applied as the prime mover. As shown in Figure 1 and Figure 1, the "open" Brayton cycle is the thermodynamic cycle for all gas turbines. Air enters the compressor inlet at ambient conditions (Point 1), is compressed (Point 2), and passes through the combustion system, where it is combined with fuel and "fired" to the maximum cycle temperature (Point 3). The heated air is expanded through the gas producer turbine section (between Points 3 and 5), where the energy of the working fluid is extracted to generate power for driving the compressor, and expanded through the power turbine to drive the load (Point 7).
Gas turbine meters are velocity meters, and the upper velocity limit is essentially unchanged by pressure. There are two main standards for turbine meters: ISO Standard 9951, Measurement of Gas Flow in Closed Conduits: Turbine Meters and OIML R32, Rotary Piston Gas and Turbine Gas Meters. An exploded view of a turbine meter is given in Figure 1. For additional information about turbine meters and their use in liquid measurement, see Inference liquid meters. Like orifice meters, turbine meters should be mounted within a meter tube (Figure 1).
Although many measurements are taken while drilling, the term MWD refers to measurements taken downhole with an electromechanical device located in the bottomhole assembly (BHA). Telemetry methods had difficulty in coping with the large volumes of downhole data, so the definition of MWD was broadened to include data that were stored in tool memory and recovered when the tool was returned to the surface. Power systems in MWD generally may be classified as one of two types: battery or turbine. Both types of power systems have inherent advantages and liabilities. In many MWD systems, a combination of these two types of power systems is used to provide power to the MWD tool so power will not be interrupted during intermittent drilling-fluid flow conditions.
Flow measurement begins with a properly operating flowmeter; however, measurement procedures and correct flow calculations equally contribute to good overall system performance. Guidelines for liquid hydrocarbon measurement are detailed in the American Petroleum Institute's (API's) Manual of Petroleum Measurement Standards (MPMS), a comprehensive, ongoing publication in which chapters are periodically revised and then released. Commonly referenced standards include: Chap. 4 "Proving Systems," Chap. 5 "Metering," Chap. The information in this chapter covers the characteristics of three types of flowmeters that are commonly used for the measurement of liquid hydrocarbons: the selection criteria for a flowmeter, the basics of field meter proving, and specifics on the design and operation of a lease automated custody transfer (LACT) system. Liquid flowmeters can be classified in two general areas: (1) a positive displacement meter that continuously divides the flowing stream into known volumetric segments, isolating the segments momentarily and returning it to the flowing stream while counting the number of displacements; and (2) an inference meter that "infers" flow by measuring some dynamic property of the flowing stream.
Meter proving is the physical testing of the performance of a liquid meter in a liquid service. The main purpose of the test is to assure accuracy. The basic principles of proving a liquid meter are the same whether it is a Coriolis meter, turbine meter, or a positive displacement meter. Meter factor prover known volume/meter reading. When proving a meter, the process-fluid conditions must be as stable as possible throughout the proving process.
It is widely accepted that global natural gas demand will continue to grow for the foreseeable future, possibly doubling every decade. Major new upstream developments, together with midstream transportation systems and downstream feedstock projects, are already progressing in all world areas. As this gas revolution evolves, there will be a dramatic rise in the requirement for high-accuracy measurement at every point in the gas value chain (Figure 1). Within these categories, there is a huge array of different gas-metering applications and a similar number of potential solutions. This can lead to confusion when selecting the optimum solution for the application.
Pump drivers include electric motors, steam turbines, expansion turbines, gas turbines, and internal combustion engines. Three-phase alternating-current induction motors are the most commonly used driver for pumps because of the desirable characteristics of electricity as a power source and because the standard rotative speeds (1,750 and 3,500 rev/min) are well suited for driver centrifugal pumps. Large gas plants containing boilers use steam turbines to drive large pumps such as lean-oil pumps, boiler feed-water pumps, and solvent-circulation pumps. It is a common practice to select a turbine rated at pump speed and power requirements and to rely on the inherent flexibility of the turbine to provide for a margin of error. High-pressure process streams in gas plants commonly have pressure reduced for further processing.
Vapor-dominated resources use conversion systems where the produced steam is expanded directly through a turbine. Liquid-dominated resources use either flash-steam or binary systems, with the binary conversion system predominately used with the lower temperature resources. When the geothermal resource produces a saturated or superheated vapor, the steam is collected from the production wells and sent to a conventional steam turbine (see Figure 1). Before the steam enters the turbine, appropriate measures are taken to remove any solid debris from the steam flow, as well as corrosive substances contained in the process stream (typically removed with water washing). If the steam at the wellhead is saturated, steps are taken to remove any liquid that is present or forms prior to the steam entering the turbine.
Condition Monitoring usually analyzes each tag individually using limit values provided by human experts. This results in false alarms and unhealthy conditions that are not alarmed. Using machine learning techniques, all tags on a piece of equipment, such as gas and steam turbines, compressors, pumps, valves, heat exchangers and so on, can be analyzed as a single coherent whole to draw conclusions about its current state of health. A mathematical model of the relevant tag is learned using the other tags of the same equipment. This model represents the equipment as a whole and its behavior when it is operating as it should as only data from known healthy operations are used for learning. The computed value is compared to the measured value. If they agree, the current state is healthy. If they do not, an alarm is released, and a maintenance activity must follow. This method is proven to be far more successful than standard condition monitoring in preventing false alarms and alarming unhealthy states.