mud pump gear ratio calculator made in china

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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.

Lateral vibration occurs when the drill string is moving laterally, perpendicular to the hole centre of bore hole. Since the MWD transmitter or pulser is a mechanical device, the lateral vibration disturbs the pulser movement to create signal, especially the poppet valve type pulser. Rotary pulser is less affected by lateral vibration.

Lateral vibration may be caused by axial vibration or lack of drill string stabilization due to enlarge bore hole. The same as axial vibration, to reduce lateral vibration the string SPM and weight on bit must be altered.

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.

Important to note that the surface computer has been programmed to detect a signal with certain signal width and certain signal separation. When the computer sees this wider signal and/or two signals which are not correctly separated, the computer will set these signal as false ones and will not be decoded or decoded incorrectly.

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.

Based on the informatization and intelligent construction of an oilfield, this paper proposes a new method for calculating inflow performance relationship in sucker rod pump wells, which solves the limitations of current IPR curve calculation method in practical application. By analyzing the forming principle of the dynamometer card, the plate of abnormal dynamometer card is created innovatively, and the recognition model of abnormal dynamometer card based on “feature recognition” is established to ensure the accuracy of the dynamometer card. By analyzing the curvature of each point on the curve of downhole pump dynamometer card, the opening and closing points of standing valve and traveling valve are determined, and the models for calculating fluid production and bottom hole flowing pressure are established to obtain the data of fluid production and bottom hole flowing pressure of sucker rod pump wells. Finally, a calculation model of inflow performance relationship fitted with the calculated fluid production and bottom hole flowing pressure data based on genetic algorithm is established to realize calculation of oil well inflow performance relationship curve. The field application and analysis results show that the inflow performance relationship curve calculated by the model in this paper fits well with the measured data points, indicating that the calculation model has high accuracy and can provide theoretical and technical support for the field. Moreover, the real-time acquisition of dynamometer cards can provide real-time data source for this method, improve the timeliness of oil well production analysis, and help to reduce the production management costs and improve the production efficiency and benefit.

In view of the above problems, based on the informatization and intelligent construction of the oilfield [21, 22], this paper studies the calculation method of inflow performance relationship in sucker rod pump wells based on real-time monitoring dynamometer card. By analyzing the forming principle of the dynamometer card, considering the abnormal changes of load that may occur in the four stages of the dynamometer card formation, the abnormal dynamometer card plate and identification model are established to ensure the accuracy of the dynamometer card. According to the working principle of the pump and the physical meaning of opening and closing points of pump valves, the opening and closing points of standing valve and traveling valve are determined by analyzing the curvature of each point on the curve of downhole pump dynamometer card, and the calculation models of fluid production and bottom hole flow pressure are established to obtain the data of fluid production and bottom hole flow pressure. Based on the Bendakhlia model, a model for calculating inflow performance relationship fitted with the calculated fluid production and bottom hole flowing pressure data based on genetic algorithm is established to realize the calculation of oil well inflow performance relationship curve.

The BC section of the normal dynamometer card is the upstroke section after the end of the initial deformation of the sucker rod string. The traveling valve is closed, the standing valve is opened, and the load is relatively stable. If it is affected by sand production or rod string vibration, there will be a small amplitude of fluctuation; if affected by gas or the traveling valve leakage, the load will be delayed or unloaded in advance, and the length of stable load in the BC section will be shortened; if the pump plunger comes out of the working cylinder, the load will rapidly reduce to the DA section of the downstroke, which belongs to the normal dynamometer card under the influence of typical working conditions.

The DA section of the normal dynamometer card is the stable downward stage after the end of the initial deformation of the sucker rod string. The traveling valve is opened, and the standing valve is closed. The load is relatively stable and close to the theoretical minimum load. Similar to the BC section, if affected by sand production or rod string vibration, there will be a small amplitude of fluctuation; if affected by fluid pound, gas or valve leakage, unloading will be delayed, and the length of stable load in the DA section will be shortened.

where is the actual fluid production of the oil well (m3/d), is the volume factor of the mixed fluid (m3/m3), is the effective stroke of the plunger, which can be determined by the downhole pump dynamometer card (m), is the stroke of the pumping unit (min-1), and is the cross-sectional area of the plunger (m2).

The volume factor of mixed fluid is the ratio of the volume of surface fluid to the volume of mixed fluid in tubing under formation conditions [24], which is related to the parameters such as pressure , temperature , dissolved gas oil ratio , crude oil volume factor , and water volume factor . The calculation formula is as follows:

In the upstroke, the plunger goes up, the traveling valve closes, and the standing valve opens when the submergence pressure is greater than the pressure in the pump barrel. After the completion of the upstroke loading, the load on the bottom end face of the sucker rod string (the load of the upstroke in the downhole pump dynamometer card) is as follows:

where is the load on the bottom end face of the sucker rod string from the opening to closing of the standing valve (N), is the discharge pressure of the pump, which can be calculated using the correlation formula of multiphase pipe flow in wellbore (Pa), and are the cross-sectional areas of the plunger and the lower end face of the sucker rod string, respectively (m2), is the submergence pressure (Pa), is the pressure drop caused by the fluid passing through the standing valve hole (Pa), is the plunger weight (N), and is the friction force between the plunger and pump barrel (N).

In the downstroke, the plunger moves down, the standing valve closes, and the traveling valve opens when the pressure in the pump barrel is greater than the fluid column pressure above the plunger. After the unloading of the downstroke, the load on the bottom end face of the sucker rod string (the load of the downstroke in the downhole pump dynamometer card) is as follows:

where is wellhead oil pressure (Pa), is the density of fluid in the oil pipe (kg/m3), is the acceleration of gravity (m/s2), and is the pump setting depth (m).

Therefore, the bottom hole flowing pressure can be obtained from the load difference () between the upstroke and downstroke of the downhole pump dynamometer card.

The solution method of the downhole pump dynamometer card is as follows. Taking the surface position and load (surface dynamometer card) as the boundary conditions, the Fourier series method was used to solve the one-dimensional damped wave equation [26–28].

One of the key steps in calculating the effective stroke and the load difference between the upstroke and downstroke of the downhole pump dynamometer card is to accurately determine the position of opening and closing points of pump valves [29]. According to the analysis of the downhole pump dynamometer card, the opening and closing points of the valves are located at the position where the curvature of the curve changes significantly. The opening and closing points of the standing valve are located in the upstroke section of the downhole pump dynamometer card, and the opening and closing points of the traveling valve are located in the downstroke section of the downhole pump dynamometer card. Therefore, the position of opening and closing points of traveling valve and standing valve can be determined by calculating four points with the largest curvature change in the upstroke and downstroke section.

Because there is a lot of high-frequency parts in the closed curve of downhole pump dynamometer card obtained by numerical calculation method, the five-point average method is used to eliminate or reduce the curvature change caused by it in actual calculation, so as to improve the accuracy of curvature calculation.

Step 2.The maximum value and minimum value of the abscissa and the maximum value and minimum value of the ordinate in the downhole pump dynamometer card are calculated, respectively.

Step 4.The normalized downhole pump dynamometer card is expanded along the plunger stroke, and the pump dynamometer card changes from a closed curve to a single value curve, as shown in Figure 2.

Step 3.After the emergence of the initial group, the next generation of individuals is selected according to the principle of survival of the fittest. That is to eliminate the “chromosome” with larger fitness and retain the “chromosome” with smaller fitness. For the individuals selected for breeding the next generation, the same positions of two individuals are randomly selected, and according to the cross probability , the selected positions are exchanged. After that, the mutation probability is used to perform mutation on some bits of some individuals. In this way, a new generation of groups emerged and replaced the old ones.

Step 4.In this way, after several generations of selection, crossover, and variation, the survival population has a smaller fitness than the original population. Finally, the “chromosome” with the smallest fitness is selected as the optimal value, which corresponds to the three coefficients in the calculation model of inflow performance relationship curve. The flow chart of fitting regression is shown in Figure 3.

In order to verify the accuracy of the established model and the feasibility of the genetic algorithm regression curve, a sucker rod pump well J1 in an oilfield is taken as an example for calculation and analysis. The average pressure of the reservoir is 10.4 MPa during multipoint test. The dynamometer card, bottom hole flowing pressure, and corresponding fluid production of eight points are measured. The dynamometer card is identified, and the fluid production and bottom hole flowing pressure are calculated by using the model established in this paper. The calculation results are shown in Table 1. It can be seen that the average relative error of bottom hole flowing pressure calculation is 4.06%, and the average error of fluid production calculation is 2.49%, which shows that the model has high calculation accuracy and can accurately identify the abnormal dynamometer card to ensure the accuracy of inflow performance relationship curve fitting data.

(1)By analyzing the forming principle of the dynamometer card, considering the abnormal changes of load that may occur in the four stages of dynamometer card formation, the abnormal dynamometer card plate and identification model are established to ensure the accuracy of dynamometer card data. According to the working principle of the pump and the physical meaning of the opening and closing points of the pump valves, the opening and closing points of standing valve and traveling valve are determined by analyzing the curvature of each point on the curve of downhole pump dynamometer card, and the model for calculating fluid production and bottom hole flow pressure is established to obtain the data of fluid production and bottom hole flow pressure. Finally, a method for calculating inflow performance relationship fitted with the calculated fluid production and bottom hole flowing pressure data based on genetic algorithm is established(2)The field application and analysis results show that the well inflow performance relationship curve calculated by the model in this paper fits well with the measured data points, indicating that the calculation model has high accuracy and can provide theoretical and technical support for the field. Using this model, we can fit the and values of oil wells in different periods and under different production conditions according to dynamometer card, so as to determine the most ideal IPR equation of oil wells and improve the accuracy of productivity prediction(3)Based on the informatization and intelligent construction of oilfield, the data such as surface dynamometer card, oil pressure, and casing pressure are collected in real time, which can provide real-time data source for fitting calculation of inflow performance relationship curve of oil well, improve the timeliness of oil well production analysis, and help to reduce the production management costs and improve the production efficiency and benefit

When two (or more) pumps are arranged in serial their resulting pump performance curve is obtained by adding theirheads at the same flow rate as indicated in the figure below.

Centrifugal pumps in series are used to overcome larger system head loss than one pump can handle alone. for two identical pumps in series the head will be twice the head of a single pump at the same flow rate - as indicated with point 2.

With a constant flowrate the combined head moves from 1 to 2 - BUTin practice the combined head and flow rate moves along the system curve to point 3. point 3 is where the system operates with both pumps running

When two or more pumps are arranged in parallel their resulting performance curve is obtained by adding the pumps flow rates at the same head as indicated in the figure below.

Centrifugal pumps in parallel are used to overcome larger volume flows than one pump can handle alone. for two identical pumps in parallel and the head kept constant - the flow rate doubles compared to a single pump as indicated with point 2

Note! In practice the combined head and volume flow moves along the system curve as indicated from 1 to 3. point 3 is where the system operates with both pumps running

In practice, if one of the pumps in parallel or series stops, the operation point moves along the system resistance curve from point 3 to point 1 - the head and flow rate are decreased.

Multilateral-horizontal-well drilling is an efficient approach for stimulating shallow, low-permeability, marginal, and coalbed-methane (CBM) reservoirs. Radial-jet-drilling (RJD) technology, which uses a high-pressure water jet, aims to drill tens of laterals from a vertical wellbore. Hydraulics design is essential for successful field-drilling operations. However, detailed hydraulics calculations and design methods have not yet been published.

The hydraulics calculations corresponded well with the field data. The model error was within 8%. The pressure loss of the high-pressure hose and jet bit represents a large proportion of the RJD-system pressure loss (41.2 and 55.8%, respectively). According to the operation profile, the calculated pump pressure will help the field engineer to estimate the working status of downhole tools. The results show that the pump flow rate should be optimized for different well configurations. The optimum flow-rate range was determined by the minimum lateral-extending force, minimum rock-breaking jet-bit-pressure drop, and minimum equipment-safety working pressure. To maximize the rate of penetration (ROP), the largest flow rate within that interval was selected as the optimum flow rate. A flow rate of 57.24 L/min was optimal for the case well.

The present work provides a viable and detailed hydraulics-calculation model and design method for RJD, and may be used for both short-term troubleshooting and long-term operation planning. It can serve as a guide for the development of safer and more-effective RJD practices.