mud pump rpm calculator made in china

The shaft power - the power required transferred from the motor to the shaft of the pump - depends on the efficiency of the pump and can be calculated as Ps(kW) = Ph(kW)/ η (3)

Rig pump output, normally in volume per stroke, of mud pumps on the rig is  one of important figures that we really need to know because we will use pump out put figures to calculate many parameters such as bottom up strokes,  wash out depth, tracking drilling fluid, etc. In this post, you will learn how to calculate pump out put for triplex pump and duplex pump in bothOilfield and Metric Unit.

This article will focus on understanding of MWD signal decoding which is transmitted via mud pulse telemetry since this method of transmission is the most widely used commercially in the world.

As a basic idea, one must know that transmitted MWD signal is a wave that travels through a medium. In this case, the medium is mud column inside the drill string to mud pumps. Decoding is about detecting the travelling wave and convert it into data stream to be presented as numerical or graphical display.

The signal is produced by downhole transmitter in the form of positive pulse or negative pulse. It travels up hole through mud channel and received on the surface by pressure sensor. From this sensor, electrical signal is passed to surface computer via electrical cable.

Noise sources are bit, drill string vibration, bottom hole assemblies, signal reflection and mud pumps. Other than the noises, the signal is also dampened by the mud which make the signal becomes weak at the time it reaches the pressure sensor. Depth also weaken the signal strength, the deeper the depth, the weaker the signal detected.

Rock bit may create tri-lobe pattern. This pattern is created by the cones of the bit on the bottom of the hole. While drilling, the bit’s cones ride along this tri-lobe pattern and makes the bit bounce or known as axial vibration. As the bit bounces, back pressure is produced at the bit nozzles and transmitted to surface. The frequency of the noise created by bit bounce correlates with bit RPM. The formula to calculate its frequency is 3*(bit RPM)/60. When the bit bounce frequency match with MWD signal frequency, decoding is affected.

To overcome bit noise or bit bounce, the tri-lobe pattern must be destroyed by altering bit RPM and weight on bit. Severe bit bounce is not only affecting decoding but may also destroy bottom hole assemblies and the bit itself.

BHA components that have moving mechanical parts such as positive mud motor and agitator create noise at certain frequency. The frequency produced by these assemblies depends on the flow rate and the lobe configuration. The higher the flow rate and the higher the lobe configuration creates higher noise frequency.

Thruster, normally made up above MWD tool, tends to dampen the MWD signal significantly. It has a nozzle to use mud hydraulic power to push its spline mandrel – and then push the BHA components beneath it including the bit – against bottom of the hole. When the MWD signal is passing through the nozzle, the signal loses some of its energy. Weaker signal will then be detected on surface.

The common sources of signal reflection are pipe bending, change in pipe inner diameter or closed valve. These are easily found in pipe manifold on the rig floor. To avoid the signal reflection problem, the pressure sensor must be mounted in a free reflection source area, for example close to mud pumps. The most effective way to solve this problem is using dual pressure sensors method.

Mud pump is positive displacement pump. It uses pistons in triplex or duplex configuration. As the piston pushes the mud out of pump, pressure spikes created. When the piston retracts, the pressure back to idle. The back and forth action of pistons produce pressure fluctuation at the pump outlet.

Pressure fluctuation is dampened by a dampener which is located at the pump outlet. It is a big rounded metal filled with nitrogen gas and separated by a membrane from the mud output. When the piston pushes the mud the nitrogen gas in the dampener will be compressed storing the pressure energy; and when the piston retracts the compressed nitrogen gas in the dampener release the stored energy. So that the output pressure will be stable – no pressure fluctuation.

The dampener needs to be charged by adding nitrogen gas to certain pressure. If the nitrogen pressure is not at the right pressure, either too high or too low, the pump output pressure fluctuation will not be stabilized. This pressure fluctuation may match the MWD frequency signal and hence it disturbs decoding, it is called pump noise.

When the pump noise occurs, one may simply change the flow rate (stroke rate) so that the pump noise frequency fall outside the MWD frequency band – and then apply band pass frequency to the decoder.

The formula to calculate pump noise frequency is 3*(pump stroke rate)/60 for triplex pump and 2*(pump stroke rate)/60 for duplex pump. The rule of thumb to set up dampener pressure charge is a third (1/3) of the working standpipe pressure.

Sometime the MWD signal is not detected at all when making surface test although the MWD tool is working perfectly. This happen whenever the stand pipe pressure is the same with the pump dampener pressure. Reducing or increasing test flow rate to reduce or increase stand pipe pressure helps to overcome the problem.

When the MWD signal wave travels through mud as the transmission medium, the wave loses its energy. In other words, the wave is giving some energy to the mud.

The mud properties that are affecting MWD signal transmission is viscosity and weight. The increasing mud weight means there is more solid material or heavier material in the mud. Sometimes the mud weight increment is directly affecting mud viscosity to become higher. The MWD signal wave interacts with those materials and thus its energy is reduced on its way to surface. The more viscous the mud and the heavier the mud, the weaker the signal detected on surface.

Aerated mud often used in underbalance drilling to keep mud influx into the formation as low as possible. The gas injected into the mud acts as signal dampener as gas bubble is compressible. MWD signal suffers severely in this type of mud.

Proper planning before setting the MWD pulser gap, flow rate and pump dampener pressure based on mud properties information is the key to overcome weak signal.

The further the signal travels, the weaker the signal detected on the surface. The amount of detected signal weakness ratio compare to the original signal strength when it is created at the pulser depends on many factors, for example, mud properties, BHA component, temperature and surface equipment settings.

Researchers have shown that mud pulse telemetry technologies have gained exploration and drilling application advantages by providing cost-effective real-time data transmission in closed-loop drilling operations. Given the inherited mud pulse operation difficulties, there have been numerous communication channel efforts to improve data rate speed and transmission distance in LWD operations. As discussed in “MPT systems signal impairments”, mud pulse signal pulse transmissions are subjected to mud pump noise signals, signal attenuation and dispersion, downhole random (electrical) noises, signal echoes and reflections, drillstring rock formation and gas effects, that demand complex surface signal detection and extraction processes. A number of enhanced signal processing techniques and methods to signal coding and decoding, data compression, noise cancellation and channel equalization have led to improved MPT performance in tests and field applications. This section discusses signal-processing techniques to minimize or eliminate signal impairments on mud pulse telemetry system.

At early stages of mud pulse telemetry applications, matched filter demonstrated the ability to detect mud pulse signals in the presence of simulated or real noise. Matched filter method eliminated the mud noise effects by calculating the self-correlation coefficients of received signal mixed with noise (Marsh et al. 1988). Sharp cutoff low-pass filter was proposed to remove mud pump high frequencies and improve surface signal detection. However, matched filter method was appropriate only for limited single frequency signal modulated by frequency-shift keying (FSK) with low transmission efficiency and could not work for frequency band signals modulated by phase shift keying (PSK) (Shen et al. 2013a).

In processing noise-contaminated mud pulse signals, longer vanishing moments are used, but takes longer time for wavelet transform. The main wavelet transform method challenges include effective selection of wavelet base, scale parameters and vanishing moment; the key determinants of signal correlation coefficients used to evaluate similarities between original and processed signals. Chen et al. (2010) researched on wavelet transform and de-noising technique to obtain mud pulse signals waveform shaping and signal extraction based on the pulse-code information processing to restore pulse signal and improve SNR. Simulated discrete wavelet transform showed effective de-noise technique, downhole signal was recovered and decoded with low error rate. Namuq et al. (2013) studied mud pulse signal detection and characterization technique of non-stationary continuous pressure pulses generated by the mud siren based on the continuous Morlet wavelet transformation. In this method, generated non-stationary sinusoidal pressure pulses with varying amplitudes and frequencies used ASK and FSK modulation schemes. Simulated wavelet technique showed appropriate results for dynamic signal characteristics analysis.

As discussed in “MPT mud pump noises”, the often overlap of the mud pulses frequency spectra with the mud pump noise frequency components adds complexity to mud pulse signal detection and extraction. Real-time monitoring requirement and the non-stationary frequency characteristics made the utilization of traditional noise filtering techniques very difficult (Brandon et al. 1999). The MPT operations practical problem contains spurious frequency peaks or outliers that the standard filter design cannot effectively eliminate without the possibility of destroying some data. Therefore, to separate noise components from signal components, new filtering algorithms are compulsory.

Early development Brandon et al. (1999) proposed adaptive compensation method that use non-linear digital gain and signal averaging in the reference channel to eliminate the noise components in the primary channel. In this method, synthesized mud pulse signal and mud pump noise were generated and tested to examine the real-time digital adaptive compensation applicability. However, the method was not successfully applied due to complex noise signals where the power and the phases of the pump noises are not the same.

Jianhui et al. (2007) researched the use of two-step filtering algorithms to eliminate mud pulse signal direct current (DC) noise components and attenuate the high frequency noises. In the study, the low-pass finite impulse response (FIR) filter design was used as the DC estimator to get a zero mean signal from the received pressure waveforms while the band-pass filter was used to eliminate out-of-band mud pump frequency components. This method used center-of-gravity technique to obtain mud pulse positions of downhole signal modulated by pulse positioning modulation (PPM) scheme. Later Zhao et al. (2009) used the average filtering algorithm to decay DC noise components and a windowed limited impulse response (FIR) algorithm deployed to filter high frequency noise. Yuan and Gong (2011) studied the use of directional difference filter and band-pass filter methods to remove noise on the continuous mud pulse differential binary phase shift keying (DBPSK) modulated downhole signal. In this technique, the directional difference filter was used to eliminate mud pump and reflection noise signals in time domain while band-pass filter isolated out-of-band noise frequencies in frequency domain.

Other researchers implemented adaptive FIR digital filter using least mean square (LMS) evaluation criterion to realize the filter performances to eliminate random noise frequencies and reconstruct mud pulse signals. This technique was adopted to reduce mud pump noise and improve surface received telemetry signal detection and reliability. However, the quality of reconstructed signal depends on the signal distortion factor, which relates to the filter step-size factor. Reasonably, chosen filter step-size factor reduces the signal distortion quality. Li and Reckmann (2009) research used the reference signal fundamental frequencies and simulated mud pump harmonic frequencies passed through the LMS filter design to adaptively track pump noises. This method reduced the pump noise signals by subtracting the pump noise approximation from the received telemetry signal. Shen et al. (2013a) studied the impacts of filter step-size on signal-to-noise ratio (SNR) distortions. The study used the LMS control algorithm to adjust the adaptive filter weight coefficients on mud pulse signal modulated by differential phase shift keying (DPSK). In this technique, the same filter step-size factor numerical calculations showed that the distortion factor of reconstructed mud pressure QPSK signal is smaller than that of the mud pressure DPSK signal.

Study on electromagnetic LWD receiver’s ability to extract weak signals from large amounts of well site noise using the adaptive LMS iterative algorithm was done by (Liu 2016). Though the method is complex and not straightforward to implement, successive LMS adaptive iterations produced the LMS filter output that converges to an acceptable harmonic pump noise approximation. Researchers’ experimental and simulated results show that the modified LMS algorithm has faster convergence speed, smaller steady state and lower excess mean square error. Studies have shown that adaptive FIR LMS noise cancellation algorithm is a feasible effective technique to recover useful surface-decoded signal with reasonable information quantity and low error rate.

Different techniques which utilize two pressure sensors have been proposed to reduce or eliminate mud pump noises and recover downhole telemetry signals. During mud pressure signal generation, activated pulsar provides an uplink signal at the downhole location and the at least two sensor measurements are used to estimate the mud channel transfer function (Reckmann 2008). The telemetry signal and the first signal (pressure signal or flow rate signal) are used to activate the pulsar and provide an uplink signal at the downhole location; second signal received at the surface detectors is processed to estimate the telemetry signal; a third signal responsive to the uplink signal at a location near the downhole location is measured (Brackel 2016; Brooks 2015; Reckmann 2008, 2014). The filtering process uses the time delay between first and third signals to estimate the two signal cross-correlation (Reckmann 2014). In this method, the derived filter estimates the transfer function of the communication channel between the pressure sensor locations proximate to the mud pump noise source signals. The digital pump stroke is used to generate pump noise signal source at a sampling rate that is less than the selected receiver signal (Brackel 2016). This technique is complex as it is difficult to estimate accurately the phase difference required to give quantifiable time delay between the pump sensor and pressure sensor signals.

As mud pulse frequencies coincide with pump noise frequency in the MPT 1–20 Hz frequency operations, applications of narrow-band filter cannot effectively eliminate pump noises. Shao et al. (2017) proposed continuous mud pulse signal extraction method using dual sensor differential signal algorithm; the signal was modulated by the binary frequency-shift keying (BFSK). Based on opposite propagation direction between the downhole mud pulses and pump noises, analysis of signal convolution and Fourier transform theory signal processing methods can cancel pump noise signals using Eqs. 3 and 4. The extracted mud pulse telemetry signal in frequency domain is given by Eqs. 3 and 4 and its inverse Fourier transformation by Eq. 4. The method is feasible to solve the problem of signal extraction from pump noise,

These researches provide a novel mud pulse signal detection and extraction techniques submerged into mud pump noise, attenuation, reflections, and other noise signals as it moves through the drilling mud.

Road planers, dredges, and other equipment require power from some type of engine to perform their designed function. Without a power take-off, it would be necessary to add a second engine to provide the power required to run hydraulic pumps and other driveline attached equipment.

Power take-offs allow mobile crushing plants, road milling machines, and other vehicles to perform auxiliary functions without needing an additional engine to power them. A PTO is a device (a mechanism) usually seated on the flywheel housing, which transfers power from the driveline (engine) to a secondary application. In most cases, this power transfer applies to a secondary shaft that drives a hydraulic pump, generator, air compressor, pneumatic blower, or vacuum pump.

Keep in mind that these calculations only apply to PTOs that drives a hydraulic pump. In cases where power take-off is providing power to a different type of drive component, it will require the manufacturer’s specifications of the driven element.

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