Drilling Monitoring System: Mud Motor Condition and Performance Evaluation
Item Type Conference Paper
Authors Koulidis, Alexis;Abdullatif, Mohammed Abdulhamid;Ahmed, Shehab
Citation Koulidis, A., Abdullatif, M., & Ahmed, S. (2023). Drilling Monitoring System: Mud Motor Condition and Performance Evaluation. Day 1 Sun, February 19, 2023. https://doi.org/10.2118/213422-ms Eprint version Post-print
DOI 10.2118/213422-ms
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Given Name Middle Name Surname Company
Alexis Koulidis King Abdullah University of Science and
Technology
Mohammed Abdullatif King Abdullah University of Science and
Technology
Shehab Ahmed King Abdullah University of Science and
Technology
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Abstract
Condition monitoring of bottom hole assembly (BHA) is essential during the different lifecycle stages of the drilling process, whether during the planning, implementation, or post-job failure analysis. Mud motor condition evaluation can assist in preventing mud motor damage and increasing drilling efficiency. This paper aims to develop a monitoring system that combines field data, data analytics and physics-based modelling to evaluate mud motor condition and performance.
The drilling monitoring system is a set of modelling and analysis tools that utilize actual drilling data, power section performance data and drillstring design to recreate the drilling process.
An unprocessed drilling dataset is required to assess the drilling operations (rotating or sliding mode, rotating off-bottom, backreaming, connections) and reconstruct the borehole trajectory from the measured survey and duty cycle (rotating and sliding mode). Interaction of the BHA and the borehole generate side forces and bending moments along the length of the BHA that are evaluated at each depth increment during the drilling process. Generated power and efficiency of the mud motor are calculated and incorporated into the dynamic simulation.
The case study investigates two motor runs in vertical and inclined sections. Dynamic modelling and extensive data analytics assist in visualizing and correlating the input and output variables during the drilling process. The continuous evaluation of the differential pressure on the motor is the primary parameter that is investigated. The motor condition is established with a continuous wear-off test while drilling and correlation matrices to indicate a constant motor state for 135 hours.
The system accurately monitors the motor's operating efficiency, with the additional advantage that the mud motor and drill bit performance are differentiated. Precise adjustments of the drilling parameters for the optimum depth of cut positively impact motor efficiency.
In addition, an interesting observation shows that accurate modelling of downhole and surface torque provides significant insights regarding bit state. The results demonstrate that with the current methodology decrease in drilling efficiency is detected and is associated with bit wear.
The work enables the evaluation of the mud motor condition and performance by utilizing a monitoring system and actual drilling data. The drilling monitoring system is a modelling analysis tool that can provide continuous feedback to the drilling operators about the condition of the BHA. Hence, it enables a
real-time optimization process to manage mud motor condition and enhance drilling efficiency.
Introduction
Drilling optimization is a field of study that combines the complexity of actual data and physics-based models. The optimization process incorporates a pre-drilled simulation to evaluate the actual performance with physics-based models for a particular field.
Engineers focus on designing the bottomhole assembly (BHA) to enhance the drilling performance.
Part of the BHA for both vertical and inclined sections is a positive displacement motor or mud motor, which converts the hydraulic energy the drilling fluid provides to mechanical energy, which is rotation and torque. Mud motor design and performance have been a subject of research for many years since adding an additional mechanical tool in the BHA, on the other hand, provides performance but, on the contrary, depending on the duty cycle (which is a function of the operating parameters) might fail and is associated with non-productive time (El-sayed et al., 2020).
An insightful overview with regards to mud motor design and performance optimum operating parameters is provided by Samuel and Baldenko (2015). To estimate efficiency of the motor while drilling, a standard method is the wear-off test which indicates efficiency decreases as a function of operating hours.
Motor wear is primarily a function of operating hours and operating parameters. Elastomer damage reduces the motor's volumetric efficiency, resulting in a reduction of the differential pressure and, thus mechanical energy at the bit (Anyanwu et al., 2012).
Overall drilling performance is a function of mud motor efficiency (Motahhari et al., 2007; Motahhari et al., 2010) and during the optimization process, the mud motor design has to be considered to conduct a comprehensive analysis. It has been shown that the mud motor's expected output (RPM) differs from the actual and is investigated in case studies where the BHA contains a bit box that measures rotational speed (Beeh et al., 2018).
Lawal et al. (2021) utilize a surface data-driven method to identify trends to detect mud motor failure.
A mud motor damage index was created for on and off-bottom operations incorporating 45 motor runs.
The paper describes the detection of motor stalling and the corresponding changes in differential pressure, rate of penetration and torque. In addition, a supervised machine learning algorithm was utilized to classify the failure modes depending on the features.
Real-time mud motor stalling evaluation is enabled by utilizing downhole and surface data (Alameer et al., 2022). High-frequency (50 Hz) data are synchronized with low-frequency (1 Hz) surface data and the results illustrate patterns that are encountered during motor stalling. An interesting fact is that motor stalling duration is up to 10 seconds.
This paper aims to establish a semi-automated monitoring system that utilizes actual drilling data. The workflow uses physics-based modeling and data-driven analysis that contains data quality oversight, operations recognition, mud motor performance and drilling efficiency.
Methodology
Mud motor features
Power curves are the graphs that correspond to power section performance. The graphs provide the relation between torque with differential pressure and rotational speed with flow rate. Depending on the operating time and parameters, the rotational speed reacts to the differential pressure changes. The power curves are established by circulating Newtonian fluid through the power section, which differs significantly from the actual drilling fluids (non-Newtonian). While drilling an interval, the motor differential pressure is estimated by conducting a rotating off bottom procedure with rotational speed and flow rate that will be used to drill the following interval. To calculate the unknown variables (torque and rotational speed), the motor performance curves need to be digitized into datasets that can be used to fit in equation 1:
1 motor
T = c P (1)
where Tmotor [ft-lbf], ΔP is the differential pressure across stator [psi]. The rotational speed is equal (equation 2):
2
2 3
motor
N = c P +c Q (2)
where Nmotor is the motor's rotational speed [RPM] and Q is the flow rate through the power section [GPM].
The total rotational speed of the bit is calculated from (equation 3)
total string motor
N = N +N (3)
The coefficient c1 is the torque factor [ft-lb/psi] and is the torque slope. However, coefficients c2 and c3
require a curve fitting of equation (2). As it is described in the previous paragraphs, a mud motor is a mechanical downhole device that converts the hydraulic energy (HHP) of the drilling fluid into mechanical energy (HP) to the shaft. In the motor's power section, where the rotor and the stator (elastomer) are placed, the drilling fluid enters under pressure in the cavities between the stator and rotor and initiates the motor rotation. The hydraulic and mechanical energy are calculated:
1714
HHP=Q P and 2
550 60 motor HP= T N
(4)
Where Q is the flow rate [GPM], ΔP is the differential pressure on the motor [psi], T is the produced motor torque [lb-ft] and N is the rotational speed [RPM]. The rotor has a number of lobes that affect the rotational speed and the torque the motor can provide. The motor efficiency is derived from output work and input work as per equation 5:
*100 %
output HP input HHP
= = = (5)
Drillstring Torque Modelling
During the drilling of deviated and curved sections, the drillstring is in contact with the wellbore and additional torque is added to the system. To identify issues (poor hole cleaning, tight spots, etc.) or for optimization purposes, it is essential to independently differentiate the torque produced from the drill bit and drillstring. Johancsik et al. (1984) were the pioneers that focused on the wellbore friction and forces acting on the drillstring in directional wells. Wellbore friction models were developed by considering the buoyancy factor due to the wellbore being filled with drilling fluid to calculate the drillstring weight, axial forces (tension), friction forces and the normal contact forces (Aadnoy et al., 2010; Wu & Hareland, 2012;
Tahmeen et al., 2014). The incremental buoyancy weight of each component of the drillstring is (equation 6):
WB =BF W and (65.5 ) 65.5
BF= −MW (6)
where W is weight [lb/ft], MW is mud weight [ppg] and 65.5 represents the steel density [ppg]. Fig. 1 illustrates the free-body diagram and the forces acting on a pipe on an inclined surface.
Fig. 1—Forces acting on the drill string for an inclined (left) and curved section (right) where, FT[top] is the tension force on an inclined section, and depending on downwards (tripping in) or upwards (tripping out) movement, is calculated using equation 7:
[top] (cos sin ) [bottom]
T B T
F =W L a a +F (7)
where, ΔL is the length of the component [ft] and a is the inclination [o]. Considering tension from the component below to calculate the cumulative tension is essential. The dogleg angle (θ) has to be considered for a curved section as shown in equation 8.
[top]
[bottom]
sin sin cos cos
( ) ( )
( )
top bottom top bottom
T B
top bottom top bottom
T
a a a a
F W L
a a a a
F e
− −
= − −
+
(8)
The force that is produced between the contact of the pipe with the wellbore is called normal/contact force (FN), and it depends on tension acting on that incremental point and the friction coefficient and bending of the pipe. The normal contact force is equal to equation 9:
2 2
sin( ) sin( )
2 2
top bottom top bottom
N T T
F F F a W L
+ +
= + +
(9)
where, φ is azimuth. While drilling the weight on bit (WOB) must be considered in either rotating or sliding mode. The produced torque is calculated as per equation 10.
[bottom] N
T =T +F r (10)
where μ is the coefficient of friction and r the radius of the tool joints.
Data Quality and Operations Recognition
As an initial stage, the data undergo a data quality assessment with a workflow that was described by Koulidis et al. (2022) followed by drilling operations recognition by utilizing the corrected variables.
Drilling operations recognition has been a subject of interest for several years (Thonhauser, 2004; Mathis et al., 2006; Arnaout et al., 2013). A specific procedure is followed while drilling to obtain the motor differential pressure. Initially, before the drill bit touches the bottom of the wellbore and start drilling, a typical procedure is to initiate flow and rotation. This is called rotating off-bottom operations and the bit
is off-bottom. When the bit touches the bottom of the wellbore an increase in WOB and SPP is observed.
The difference between the SPP on and off-bottom is the differential pressure on the motor. The differential pressure values are a function of the motor design, the rotor and stator state, applied WOB, etc. (Ba et al., 2019).
Fig. 2—Original set of drilling data from 1600 to 5100 ft
The challenge in the algorithm is whether the system can use the standpipe pressure data points for the first interval of rotating or sliding drilling until another rotating off-bottom operation occurs. This is essential since by drilling a certain vertical distance or changing fluid density, additional hydrostatic pressure exists due to the drilling fluid. As the data are separated per operation, it is relatively easy to initiate the separation of the drilling parameters during different modes to reduce the size of the arrays.
Since a rotating off-bottom operation can take several seconds to minutes, an average value must be obtained and appended as a new variable for a specific time and depth (Fig. 3).
Fig. 3—Identified and averaged rotating off-bottom operations
In an ideal scenario, the rotating off-bottom operation is performed every 90 ft, when a single stand is
drilled, but Fig. 3 shows that in several intervals is more frequent. On our knowledge, the drill bit depth data were unavailable; thus, the algorithm recalculates as it is observed in Fig. 4. Bit depth is an important variable in estimating the distance of the drill bit from the bottom and is regarded to recognize off-bottom operation.
Fig. 4—Original and recalculated bit depth
In several cases, the depths of two individual rotating off-bottom operations that are performed might be only a few ft (1 - 2 ft), which creates confusion on the algorithm. Thus, to reduce and eliminate that effect, a condition is applied that creates a new array if the operations depths are greater than 1.5 ft.
In addition, manual interference is required from the user in cases where very low ROP is observed. The algorithm must identify the rotating/sliding drilling and off-bottom operations to calculate the differential pressure on the motor. This step is essential since if rotating or sliding drilling is initiated, the current value of the standpipe pressure must be subtracted from the last value of the standpipe pressure during rotating off-bottom to obtain the motor differential pressure.
Dataset Description
To perform a comprehensive analysis and verify the use of monitoring system, additional information is required, which are bottomhole assembly design, motor specifications, drilling parameters and well trajectory; thus, the Utah Forge open-source data are utilized. The well 16A (78)-32 is a deviated well (Fig. 5) and is located in Utah at it is an accelerating project to unlock the potential of geothermal energy.
Fig. 5—Well trajectory of 16A (78)-32
The data are obtained from various sources: drilling data, daily drilling operation reports and surveys (McLennan et al., 2021), well drilling summary (Winkler et al., 2021), well logs and downhole data (Gilmour et al., 2021). It is important to state that several variables, such as differential pressure and mechanical specific energy are available, but the novelty of the work is to conduct a continuous assessment with minimum user interference and thus each parameter must be re-evaluated.
Results and Discussions
Vertical Section - Interval 1600 to 4900 ft
On the vertical section, a mud motor is used to enhance drilling performance by providing additional torque and rpm at the bit. To drill the 12.25-in section two PDC drill bits were utilized (Table 1), a 6 5/8 mud motor 7/8 5.9 stages and a shock sub.
Table 1—Drillbits utilized to drill the interval
Type Nozzles Depth In [ft] Depth Out [ft]
PDC 6x18 1644 4552
PDC 6x18 4552 4964
Regarding the motor power curve, the extraction of c1, c2, and c3 will define the relationships in equations (1) and (2). Coefficient c1 is usually given in the vendor datasheet as the torque slope in ft-lb/psi. However, c2 and c3 are not directly available from the vendor and require a curve fitting of equation 2. The input into the regression model includes an array of independent and dependent variables. The independent variables are flow rate and differential pressure, and the dependent variable is the rotational speed. For the current motor c3 (rotational factor) is equal to 0.155 RPM/gal and c2 is -0.000009 and c1 (torque factor) is equal to 15.7 ft-lb/psi. Having computed the differential pressure and the torque factor, the torque on the bit can be calculated as shown in Fig. 6. For tortuosity zero, the surface torque is equal to the downhole torque on a vertical section.
Fig. 6—Computed motor differential pressure and torque, and stalling intervals
In Fig. 6, motor stalling or micro-stalling events are not detected. Motor stalling are events of a sudden differential pressure increase below the maximum limit. An indication of a motor stall or micro-stall is an instantaneous peak in standpipe pressure that does not correspond to a forced change in flow rate.
Standpipe pressure (SPP) peak selection is controlled by two parameters: (1) peak prominence, and (2) peak width. The time at which a stall event occurs can be defined as 𝑡𝜇𝜖 𝑡 ∀ 𝑙𝑜𝑐𝑎𝑙 𝑚𝑎𝑥𝑖𝑚𝑎 𝑜𝑓 SPP(𝑡). In case the increase in pressure is due to an increase in flow rate, then these changes in flow rate at the selected peak is used to disregard the corresponding 𝑡𝜇 as a valid stall. Fig. 7 provides insightful information regarding the drilling parameters during those intervals. In all cases, a significant increase of WOB corresponded to an increase of depth of cut (DOC) and this motor stalling. An increase of the depth of cut corresponds to higher forces acting on the bit; thus, greater torque is required. For the deepest part of the section (4800 ft) the DOC is below 1 mm compared. Even though the stalling differential pressure/torque is approximately 2200 psi/34.4 kft-lbs the stalling events are challenging to be captured in low-frequency data.
Fig. 7—Weight on bit and depth of cut while drilling the last 1000 ft interval
To evaluate the performance of the motor and the drilling efficiency, the surface and downhole mechanical specific energy are calculated for each formation drilled. Theoretically, for surface sections, the surface and downhole mechanical specific energy should intercept but due to wellbore tortuosity and vibrations, the surface MSE is slightly higher. The surface MSE is equal to (Dupriest & Koederitz, 2005; Dupriest et al., 2020):
2 2
480( )( )
4( )
( )( ) ( )( )
surface motor surface surface
T N N
MSE WOB
D D ROP
= + + (11)
where Tsurface is the surface torque (ft-lbf) and Nmotor can be calculated by knowing the flow rate and the motor flow factor q. The downhole MSE can be calculated as in equation 12 (Dupriest et al., 2020; El- sayed et al., 2020):
2 2
480( )( )
4( )
( )( ) ( )( )
motor motor surface downhole
T N N
MSE WOB
D D ROP
= + + (12)
Fig. 8 shows that except for formation 1 and 2, the energy that is required to drill the additional 6 formations is significantly higher, which corresponds to an insufficient drilling process. Those formation corresponds to the 2nd drillbit that is utilized for the interval and demonstrates lower performance.
Fig. 8—Surface and downhole mechanical specific energy for the 8 formations drilled As the elastomer deteriorates due to duty cycle loading, motor efficiency decreases with operating hours.
Fig. 9 illustrates the motor efficiency a function of time and differential pressure. The corresponding efficiency at formation 4 shows that for the differential pressure – 400 psi the efficiency is significantly lower compared to other formations. Nevertheless, motor efficiency doesn’t show any significant decrease with operating hours, which indicates the steady motor condition.
Fig. 9—Motor efficiency with respect to time and differential pressure
It is shown in Fig. 10 that MSE, ROP and motor differential pressure have high correlation across the entire section, but it is indicated at approximately 4800 ft significantly increase of MSE and decrease of ROP. The excessive WOB that is applied during drilling from 4400 ft to approximately 4550 ft initiated the bit wear.
Fig. 10—Evaluation of MSE, ROP and motor differential pressure for detection of bit wear Deviated Section - Interval 5878 to 7294 ft
The current section is the buildup interval in which four different drill bits were utilized with the same mud motor. Due to the continuous changes in the BHAs, the data are separated in four individual data sets and combined after the processing. The mud motor is a 6 ½” 7/8 5.7 stages with a fixed 1.5 deg bend sub.
The utilized drill bits and the grading are shown in Table 2.
Table 2—Drillbits utilized to drill the interval
Type Nozzles Depth In [ft] Depth Out [ft] Grading
PDC 3x14 and 3x13 5878 6360 1-2-WT-A-X-I-CT-PR
PDC 3x14 and 3x13 6360 6526 1-1-WT-A-X-I-NO-
BHA
PDC 3x13 and 4x12 6526 6945 2-2-CT-S-X-I-WT-
BHA
PDC 4x14 and 3x13 6945 7294 1-1-WT-A-X-I-NO-
BHA
The same procedure is followed as per the previous section, but the system's complexity increases since the data frequently switch from rotating to sliding. The algorithm performs excellently in identifying the rotating and sliding mode intervals and calculating the downhole torque. It is observed in Fig. 11 that the surface measured torque during sliding drilling is approximately 1000 ft-lbs since the rotating speed is 20 RPM. In specific intervals (6150 ft, 6200 ft, 6360 ft, 6950 ft, 7000 ft and 7400 ft) the surface torque increases due to the increase of downhole torque, which shows the effect of the mud motor. In general, during the drilling of the section, the data show a linear increase of torque (drillstring + mud motor).
Fig. 11—Downhole torque during rotating and sliding mode and surface measured torque
Tight spots, excessive vibrations and poor-hole cleaning can significantly increase the torque produced from the drillstring, but from the surface, measurements are challenging to identify if the additional torque is produced from the drillbit/mud motor or the drillstring. To assess and evaluate both cases, physics- based modeling is essential. The simulation utilizes the actual BHA and surveys data to estimate the torque while rotating off-bottom. An important step is that the torque values have to be incremental and represent a real-time/dynamic scenario. The borehole over-gauge in mud motors varies based on the mode of operation. In a sliding mode, the over-gauge is minimal and is believed to be a function of the formation strength and lateral stiffness of the lower end, which spans over the distance from the bit to the first contact point. On the other hand, the over-gauge in rotary mode is primarily due to the geometrical lateral offset, although formation and stiffness of the lower end will still have an effect. It is important to incorporate the estimated borehole diameter for the entire section and calculate the side forces and torque while drilling. Fig. 12 shows the actual measured torque at the surface and the combined simulated torque while rotating off-bottom with the estimated torque from the mud motor during rotating mode.
Fig. 12—Downhole torque during rotating and sliding mode and surface measured torque
It is observed that in general the actual and simulated torque match excellently. At approximately 6220 ft the simulated with the actual measurements differ significantly, which corresponds to the overall overview regarding wellbore implications. This approach indicates the necessity of automated operation recognition, mud motor torque evaluation and real-time modelling.
In total 23 layers of different lithologies were drilled and the drilling data are separated based on formations depth. Compared to the surface section, the downhole MSE is significantly lower compared to the surface MSE with a corresponding greater drilling efficiency during rotating drilling (Fig. 13). For the deeper formations, the downhole MSE increase significantly during sliding mode due to frictional forces decreased WOB and less ROP in the drill bit (Fig. 14).
Fig. 13—Surface and downhole MSE during rotating mode
Fig. 14—Surface and downhole MSE during sliding mode
Downhole data are utilized on the analysis process. As it is stated on previous research, during motor stalling, drill bit RPM, downhole torque and ROP reduce significantly. In addition, downhole acceleration data are synchronized with the surface measurements to evaluate the downhole conditions while drilling.
Fig. 15 compares the calculated downhole RPM during rotating and sliding modes and gives an overview
of the effect of rotating speed in downhole vibrations. While drilling from 5900 to 5950 ft the downhole measured RPM, in specific intervals, is significantly lower compared to calculated motor RPM. It is important to mention that the flow rate has a steady value of 550 GPM. The GyroXmed represents the median value of certain measurements and might contain the operations while off-bottom.
Fig. 15—Calculated RPM for rotating and sliding mode in comparison to downhole and surface measurements
It can be observed that during sliding mode, the vibration levels are significantly lower compared to rotating drilling. Weight on bit slightly deviates for both drilling modes, indicating that rotational speed dramatically affects the vibrations for the current BHA. High vibrations indicate more significant resultant stresses in each component of the BHA; as a result, less energy is transmitted to the drill bit and reduces the motor's life (it can affect the bearing section but not the power section). The overall efficiency of the mud motor appears constant, except few intervals in which a decrease of 3.5% is observed (Fig. 16).
Fig. 16—Efficiency illustrated for rotating and sliding mode
Conclusions
Mud motor performance and condition evaluation is significant, and it is demonstrated that a workflow that starts from data quality, followed by operations recognition and physics-based modelling can assist in understanding downhole conditions. The procedure requires minimum intervention from the user, which is mainly to elaborate on formation tops, BHA design and data separation by mud motor run. Both runs are classified as low operating hours for a mud motor, which the motor remains in good condition.
Acknowledgments
The authors would like to express gratitude to King Abdullah University of Science and Technology for funding and supporting this work.
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