IADC/SPE 127413

MWD Failure Rates Due to Drilling Dynamics Hanno Reckmann/Baker Hughes, Pushkar Jogi/Baker Hughes, Franck Kpetehoto/Baker Hughes, Sridharan Chandrasekaran/TATA Consultancy Services, John Macpherson/Baker Hughes

Copyright 2010, IADC/SPE Drilling Conference and Exhibition This paper was prepared for presentation at the 2010 IADC/SPE Drilling Conference and Exhibition held in New Orleans, Louisiana, USA, 2–4 February 2010. This paper was selected for presentation by an IADC/SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the International Association of Drilling Contractors or the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the International Association of Drilling Contractors or the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the International Association of Drilling Contractors or the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of IADC/SPE copyright.

Abstract Rig downtime due to MWD tool failure is expensive, particularly on offshore rigs with extremely high operating costs. There

are different reasons for tool failures, one being the high vibration loads on MWD tools under extreme drilling conditions.

MWD and LWD tools are usually equipped with sensors to measure and record vibrations for drilling efficiency and

optimization. The goal of the study described in this paper is to establish the link between MWD or LWD tool failure, and

drilling dynamics. With this knowledge, it is possible to reduce failure rates and costs with real-time monitoring of downhole

drilling dynamics.

To analyze the effect of drilling dynamics on MWD tool failures, a unique database of MWD runs in challenging

environments was created. This database includes vibration data recorded at 5-second intervals from more than 12,000 drilling

and reaming hours, over a total footage of 425,000 feet. In addition to unique dynamics data such as weight, torque, bending

moments and axial, lateral and tangential RMS and peak accelerations, the database included detailed run and failure reports,

and environmental information such as well profiles and drilling operations.

A statistical study using the logistic regression indicates the types of dynamic behavior most statistically significant in

MWD/LWD tool failures. These are cumulative lateral vibrations and backward whirl. Cumulative axial or tangential

acceleration appears not to be significant in current MWD/LWD tool failures.

A study on the correlation between recorded MWD tool dynamics and tool failure rates established the link between time

spent operating above a level of vibration and the probability of tool failure. This paper contains charts describing this

relationship between dynamics, operating hours, and MWD/LWD tool failure rates. These charts are valuable in operational

risk management and in developing procedures for optimal drilling practices that reduce the dynamic loads on the MWD

system, resulting in prolonged tool life and reduced cost.

Introduction In recent years, there has been growing interest from both service companies and operators on quantified MWD tool and

system reliability. BHAs are becoming longer and more complex with the addition of more MWD and LWD components, and

the well path is becoming increasingly more challenging due to length and 3D trajectory. A vibration related failure of MWD

or LWD components can result in a trip, lost tools, or a sidetrack. All of these are expensive.

As shown in the simplified chart, Figure 1, there is considerable overlap between known excitation frequencies and

response frequencies of the BHA, and it is difficult to avoid generating large dynamic loads in the BHA while drilling.

Reliable operation consists, therefore, of combining knowledge of the operating limits of MWD and LWD tools with real-time

condition monitoring.

Brehme and Travis (2008) provide an operator’s viewpoint where the main idea is to focus on the whole system instead of

individual tools to obtain the best system reliability by eliminating systematic failures. It is mentioned that the standard

measure for the statistical reliability of tools and components in comparing different vendors is MTBF, which was originally

proposed and recommended by Ng (1989).

2 IADC/SPE 127413

Figure 1: Relationship of some known excitation frequencies to the frequencies of dynamic behavior.

System failures might be due to different causes. Wand et al. (2006), identifies the root causes for failures in rotary-

steerable-systems as “planning”, “software”, “assembly”, “shock”, and so on. Here “shock” is a descriptor used when the root

cause is mechanical vibration loads on the system, and Wand’s analysis is accountable for 23 percent of the failures. In our

analysis, we found that 29 percent of failures were vibration related. These vibration related failures include mechanical

failures (for example, cracks in steel bodies), and electrical failures (for example, vibration related breaks in electrical cables).

The higher percentage of vibration related failures in our analysis can be explained by the choice of MWD runs – the tool that

transmitted and recorded the vibration data is deployed in “higher risk jobs”, where difficult drilling and vibrations are

expected.

Most reliability-based risk analysis techniques are founded on the premise that given a sample of failure times, it is

possible to derive a probability distribution function that quantifies reliability as a function of time. Time in the drilling

industry is “operating time” which could be interpreted in several different ways, see Brehme and Travis (2008). Once this

probability distribution function is known, then it is possible to estimate values such as reliability or failure probability at a

given time. This approach has been successful in many industries, but due to the unique nature of the drilling process with its

changing stress levels (because of depth-based changes in formation properties and time-based changes in drilling parameters)

the purely time-based approach is not sufficient. It is necessary to incorporate the varying levels of stress in the risk assessment

process.

In this paper, the focus is on the influence of the dynamic loads – drilling dynamics – on the reliability of MWD and LWD

BHA systems. A data-driven model is used to assess risk and stress, and a database containing a representative number of data

sets was assembled for this purpose.

Each MWD company accumulates data for calculating tool MTBF. These databases contain information on operational

parameters such as distance drilled, circulating hours, drilling hours, reaming hours, etc. Additional data about repair and

maintenance is also collected, which includes the root cause analysis in the case of tool failures. These databases generally

lack detailed information describing the dynamic loads acting on the BHA system.

For the analysis presented in this paper the databases containing operational and maintenance data were merged with

drilling dynamics data recorded by a downhole tool designed to measure, record and provide real-time information for drilling

optimization. With this combined database, it was possible to establish the link between system dynamic loads and failure rate.

Further analysis of the large data set with respect to drilling optimization also illustrates the effects of drilling dynamic

phenomena such as stick-slip and whirl on BHA stresses and drilling performance.

Data Availability and Selection Measurement tools are available to monitor motion and loads on the BHA at fixed points within the BHA. The MWD tool

used in this study was first described by Heisig et al. (1998). It is currently capable of taking 14 dynamic measurements at a

sample rate of 1,000 Hz, among them lateral, axial and tangential accelerations, torque, axial force, and bending moment. Data

is available through telemetry in real time to surface for drilling optimization. In parallel, data is stored in the downhole tool

memory with a sampling interval of 5 seconds.

Some of the measured data used for the analysis in this paper are summarized in Table 1.

IADC/SPE 127413 3

Table 1: Measurements used in the analysis

Variable Unit Description

Lateral peak acceleration peaklata , g Maximum lateral acceleration in 5 seconds, sample rate of 1,000 Hz.

Lateral 1sRMS acceleration sRMSlata 1, g Maximum 1-second RMS over 5 seconds

Axial peak acceleration peakaxa , g Maximum lateral acceleration in 5 seconds, sample rate of 1,000 Hz.

Axial 1sRMS acceleration sRMSaxa 1, g Maximum 1-second RMS over 5 seconds

Tangential peak acceleration peaka tan, g Maximum lateral acceleration in 5 seconds, sample rate of 1,000 Hz.

Tangential 1sRMS acceleration sRMSla 1tan, g Maximum 1-second RMS over 5 seconds

RPM max maxω rpm Maximum rotation rate in a 5 second interval

RPM min minω rpm Minimum rotation rate in a 5 second interval

RPM average avgω rpm Average rotation rate over 5 seconds

Bending RPM average avgβ rpm Average bending rate over 5 seconds

Torque average avgT Nm Average downhole torque (DTOB) over 5 seconds

Axial force average avgaxF , N Average downhole axial force (DWOB) over 5 seconds

Bending moment average avgB Nm Average bending moment over 5 seconds

Bending RPM is calculated from the two orthogonal bending measurements, see Macpherson et al. (1998). Axial, lateral

and tangential acceleration are obtained by adding and subtracting measurements from a pair of tri-axial accelerometers

mounted diametrically opposite and equidistant from the centerline of the tool. Axial acceleration is the acceleration

component along the tool axis. Lateral acceleration is the acceleration of the center axis of the tool in the lateral direction.

Tangential acceleration is the acceleration component generated by a rotation around the tool center axis, and is the angular

acceleration times the distance from the tool center axis.

Figure 2: Sample data set of dynamics data from a well in Beggs, Oklahoma.

4 IADC/SPE 127413

From operations with this measurement tool during a 20-month period, January 2007 to August 2008, 232 data sets were

gathered. Of these, 67 contained identified vibration related failures. Since the measurement tool is typically run for drilling

optimization in difficult and challenging drilling environments, it is a rich source of vibration related information.

The accumulated information includes vibration data recorded at a 5-second interval from more than 12,000 drilling and

reaming hours, over a total drilled and reamed footage of 425,000 feet. A sample data subset for a well in Beggs, Oklahoma, is

shown in Figure 2.

In addition to unique dynamics data, the database includes detailed run and failure reports, and environmental information

such as well profiles and drilling operations.

Data Processing

Assumptions and Criteria

The analysis contains several assumptions:

Each MWD tool is “like new” at the begin of a run;

Only MWD tools which are identified as having failed due to vibration are included;

Tool failure is actually due to mechanical vibration loads (i.e., the root-cause analysis is correct).

In the interests of timely data gathering and processing, it is assumed that the MWD tools are “new” at the beginning of

each run. In other words, it is assumed that there is no pre-existing damage to a tool. Furthermore, it is assumed that failure

was due only to vibration. Failures caused by, for example, bending due to high dogleg severity, and manufacturing, repair,

and operational issues, are already removed from the data set.

Calculated parameters

Certain information used in the analysis were calculated from the measured data. For example, one statistic of a run is the

cumulative rotary speed variance rsvC . To characterize the variation in rotation rate, the spread between maximum RPM and

minimum RPM is multiplied by the sample interval and summed over the run. The unit of this cumulative RPM variance is

revolutions.

( ) tCrsv ∆−=∑ minmax ωω ( 1)

Other information was represented in terms of “relative energy.” The power of a parameter is proportional to the square of

its value. The energy is then proportional to the integral of the power over time. The relative energy for several different

parameters, for example the vibration energy from accelerations, bending energy from bending moments, torsional energy

from torque, and so on, can thus be calculated. These relative energy values are used in the data analysis to find which

parameters correlate with failure.

txE ∆=∑2

( 2)

Other derived parameters of interest are backward and forward whirl, and the stick-slip index. Whirl, Ω, is the rotation rate

of the center of the measurement tool about the center of the hole, and is given by the difference in rotation rate, ω, and

bending rate, β:

( ) )()( ttt avgavg βω −=Ω ( 3)

The integral backward whirl parameter is calculated by selecting those intervals in which the whirl is less than -10 RPM,

and summing these time intervals over the run. Likewise, forward whirl is handled by selecting those intervals in which the

whirl rate is positive.

Stick-slip index is the ratio of rotary speed variance to average rpm:

( ) ( )avgminmax 2/)( ωωω −=tSSI ( 4)

This parameter will be zero for sliding, one for fully developed stick-slip, and greater than one for cases where the string

comes to a complete stop for a certain time per cycle.

Data analysis

Significance (p-test) with binary logistic regression The binary logistic regression method has been developed for regression analysis with binary variables, see (Hosmer 2000;

Pampel 2000). Logistic regression is extensively used in many areas including the medical and social sciences, marketing and

reliability engineering. In the case of tool failure analysis, the criterion “failure” or “non-failure” has in a mathematical sense

IADC/SPE 127413 5

the states “0” or “1” and are thus binary in nature. Logistic regression is a statistical model for predicting the probability of

success or failure based on historical data. The advantage of the logistic regression is that it can use both categorical variables

(e.g., binary 0 or 1) and numerical variables (e.g., vibration energy). Like the ordinary regression model, logistic regression

takes a linear form of predictor variables (X1, … Xn) where n is the number of predictors:

nn XXZ βββ +++= K110 . ( 5)

In the above equation, a positive regression coefficient βi means that the i-th factor has a positive influence on the

probability of success. In addition, a large regression coefficient means that the factor strongly influences the probability of

success, while a near-zero regression coefficient means that the factor has little influence on the probability of success.

The difference between the ordinary regression model and the logistic regression model is that Z is not a response variable

in the logistic regression, but Z is the input to the probability function:

)1(

1)(

Ze

Zf−+

= . ( 6)

The probability function f(Z) is the predicted value of the probability of success. Here the probability of success is the ratio

of runs without failure to the number of total runs.

One part of the binary logistic regression analysis is the significance test (p-test) of variables, which is used to decide if

variables in the data set are significant in explaining tool failures or not. The data set is analyzed to answer the question if a

high value of a parameter corresponds to a high likelihood of a tool failure. A significance of 0.05 (1 in 20) is used as defined

in Table 2.

Table 2: Interpretation of p-value

Common Language Test Threshold

Statistically Significant. Unlikely due to chance p < 0.05

Statistically Insignificant. Likely due to chance p > 0.05

The process of finding the p-value is automated in statistic analysis software packages. The estimated p-values for the

analyzed parameters are listed in Table 3 below.

Table 3: p-test results

Parameter p-value

Significant Lateral Acceleration Peak Energy 0.0002

Lateral Acceleration 1s RMS Energy 0.0003

RPM Spread [revolutions] 0.0004

Backward Whirl 0.0025

Stick Slip Index 0.0276

Not Significant Axial Acceleration 1sRMS energy 0.0560

Axial Acceleration Peak Energy 0.0649

Forward Whirl 0.1174 TOB Energy 0.2020 WOB Energy 0.2147 TOB Maximum Energy 0.3850 Tangential Acceleration 1s RMS Energy 0.4816 Bending Rate [revolutions] 0.6500

Cumulative energy due to lateral motion of MWD tools is most significant in tool failure, as are the cumulative effects of

the variance of the tool rotation speed and backward whirl. Cumulative stick-slip is also significant but better suited for

drilling efficiency studies since the relationship between rotary speed oscillations and tool failure is better described by the

cumulative variance in tool rotational speed over a run.

The p-test showed no significant correlation between cumulative axial acceleration, tangential acceleration or bending rate

and MWD failure. This does not mean these parameter are unrelated to MWD tool failures but rather that their relationship to

tool failure may be more that of short-lived and localized events.

6 IADC/SPE 127413

Results

The peak lateral and 1-second RMS values of the lateral acceleration both show a highly significant correlation with MWD

tool failure, and are related. The graph below in Figure 3 shows an example of the correlation between these two parameters

over all 232 data sets. The logarithmic color scale is the accumulated time of values in the 0.2g bins. The result is a linear trend

between the two measurements up to about 18g in lateral peak acceleration. After 18g, there is a wider spread of data, but this

wider spread represents less than one cumulative hour and is indicative of short-lived events such as impacts. The linear graph

in the right-hand diagram of Figure 3 shows the relationship between the two lateral acceleration measurements (up to 18g

peak lateral acceleration).

This correlation indicates that either a 1-second RMS measurement, or a peak acceleration measurement (or both) can be

used for condition monitoring of an MWD BHA.

Correlation of peak to 1sRMS lateral acceleration

y = 0.2362x

0

1

2

3

4

5

6

7

8

9

10

0 5 10 15 20 25

Peak Lateral Acceleration [g]

1s

RM

S L

ate

ral

Acc

ele

rati

on

[g

]

Figure 3: Correlation of lateral peak acceleration to lateral 1sRMS acceleration

Tool Failures To establish a map from time and vibration load to probability of tool failure, the data was processed in four steps for each

mode of vibration:

1. For each run: determine the amount of time spent in each 1g bucket. The last bucket contains the time spent operating

with more than 50g.

2. For each run: determine the time above an acceleration threshold. For example, the first bucket contains the time

above 0g (i.e., all drilling and reaming time), the next bucket contains the time above a 1g threshold, and so on.

3. For all runs: in a time vs. vibration matrix, total the runs with failure. For example, if the run ended in failure and

operated with 120 hours above 0g, and 80 hours above 1g, etc., then the count for cells (0, 120) and (1, 80) are

incremented by one. The same process is repeated for the non-failure runs.

4. Calculate the ratio of runs-with-failure to total-number-of-runs in each bucket in the time versus vibration matrix.

This result is shown in Figure 4, with a 2D smoothing and interpolation function applied.

Figure 4: Failure rate (0 to 1) for time operating at or above a lateral 1sRMS acceleration threshold

Tim

e [h]

IADC/SPE 127413 7

Discussion

The white area in the failure rate versus lateral 1second RMS acceleration and time diagram represent combinations of

parameters (g-threshold and time) where no data is available. These combinations are never reached because they are either

physically not possible or the tools had already failed. The boundary of the colored area, therefore, represents the operating

limit of the tools – it is where tools are overloaded and thus fail with the highest probability. The probability of a failure then

decreases to lower values in the time-load parameter space.

Within the area with lower failure rate, the minimum is a failure rate of about 0.2. The reason is that the failed runs do not

only operate at high acceleration levels, but also spend a portion of time at low acceleration levels. In addition, due to variance

in operational conditions and variance in manufacturing and repair (in other words, real data), there are also always some

failures at low operating times, and thus the failure rates do not reach zero. It could be generalized that below 1.5g for the

lateral 1sRMS acceleration, lateral accelerations do not harm BHAs. Failures at these low lateral accelerations have a cause

other than vibration.

Figure 5: Failure rate versus lateral 1sRMS acceleration and time, fitted with a parametrical model

It is possible to fit an analytic function to the surface in the time, acceleration and failure rate space. Based on this analytic

function, tables can be generated from which it is possible to estimate the probability of failure given the acceleration history

of a tool. The probability of failure P(f) generated from the analytical fitting function is tabulated in Table 4.

Table 4: Failure rate versus lateral 1sRMS acceleration threshold and time (hours)

P(f) >0.5 >1 >1.5 >2 >2.5 >3 >3.5 >4 >4.5 >5 >5.5 >6 >6.5 >7 >7.5

1.0 ∞ ∞ ∞ ∞ 126 60 27 15 9.5 6.5 4.5 3.1 2.1 1.3 0.7

0.9 ∞ ∞ ∞ 150 110 48 23 13 8.7 5.9 4.1 2.9 1.9 1.2 0.6

0.8 ∞ ∞ ∞ 143 88 38 19 12 7.8 5.4 3.7 2.6 1.7 1.0 0.5

0.7 ∞ ∞ ∞ 123 64 29 16 11 6.9 4.8 3.3 2.3 1.5 0.8 0.3

0.6 ∞ ∞ 134 87 42 22 13 8.5 5.8 4.1 2.8 1.9 1.2 0.6 0.1

0.5 143 118 83 47 25 15 9.6 6.5 4.5 3.2 2.2 1.4 0.8 0.3

0.4 70 50 32 19 12 8.4 5.8 4.1 2.8 1.9 1.2 0.6 0.2

0.3 11 8.1 5.9 4.3 3.1 2.1 1.4 0.8 0.3

The above table indicates the probability of failure of a tool if it has operated for an amount of time (in hours) above a

vibration threshold (in grms). For example, an MWD tool which has operated for 4.8 hours above 5 grms has a 70% chance of

failure. Here the grms measurement is defined as the maximum 1-second RMS in a 5 second window.

The failure rate table can be used in several ways. A first possibility is to calculate what the actual probability of failure is,

based on the accumulated data during a run. A second possibility is to estimate the remaining time at an anticipated load until a

given failure rate is reached. A third possibility would be to calculate the acceptable vibration envelope for a given failure rate

and time.

The failure rate table, therefore, provides a powerful method of tracking the condition of an MWD BHA. It relates current

vibration level to likelihood of failure, and provides an estimate of “remaining lifetime” to failure. For example, consider an

MWD tool which has experienced conditions of 4grms for the previous 4.5 hours, and which is currently experiencing lateral

vibrations above this level. If the out-of-specification (OOS) limit is set at a probability of failure of 70%, then the tool may

only be operated in the same environment for another 6.5 hours before it becomes OOS. Alternatively, if the environment

Time [h] T

ime [

h]

8 IADC/SPE 127413

becomes more benign (due to a lithology change or an operating parameter change) so that the MWD BHA is subject to less

than 2grms lateral acceleration, then the tool is unlikely to reach an OOS condition.

Cumulative rotary speed variance The study found that the third most significant parameter correlating to failure is the cumulative rotary speed variance. The

relationship between cumulative rotary speed variance over a tool run, and the probability of failure, is shown in Table 5. An

analysis of the individual tool sizes shows that the relationship between cumulative rotary speed variance and tool failure is the

strongest for the 4.75” tool size. The bigger tool sizes also show this trend but with a different slope, and the failure rate for the

bigger tool sizes is less dependent on the cumulative rotary speed variance. Therefore, in terms of cumulative rotary speed

variance, different tool sizes should be treated differently.

Table 5: Probability of failure P(f) in respect to cumulative rpm variations for tool size 4.75”

P(f) 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

rsvC 169,500 339,000 508,500 678,000 847,500 1,017,000 1,186,400 1,355,900

Unlike the stick-slip index, the rpm variance should be similar above and below a motor. A discussion of the relationship

of stick-slip index (SSI) to vibration is given in the drilling performance section, below.

Whirl The study found that backward whirl is also statistically significant in MWD tool failures. Whirl in MWD tools can result in

high cyclic bending stresses and also in high lateral accelerations. The probability of failure criterion for backward whirl with

a whirl rate smaller than -10 RPM is provided in Table 6. The short time spans of whirl to reach high probabilities of failure

point out the destructive effect of backward whirl on BHAs. This means that real time monitoring of the backward whirl rate

and timely action to prevent whirl can significantly prolong the life of the BHA.

Table 6: Probability of failure P(f) in respect to time of backward whirl (hours)

P(f) 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

10−<βt 1.6 2.7 3.8 4.8 5.9 7.0 8.0 9.1

Drilling performance Due to the large volume of data gathered it is possible to examine the relationship between the various parameters, such as

stick-slip index and peak lateral vibrations, as shown in Figure 6. It should be realized that such studies are of large data sets

and indicate only what will happen on average.

Figure 6: Data distribution and ROP versus Stick-Slip Index and 1sRMS Lateral Acceleration

IADC/SPE 127413 9

The color in the left diagram of Figure 6 represents the amount of data falling within a vibration, stick-slip cell, and uses a

normalized logarithmic scale. A value of 1 for the stick-slip index (x-axis) indicates fully developed stick-slip (the bit rpm

approaches zero). It is interesting to note that high lateral vibration occurs at low values of stick-slip – in other words, a non-

zero value of the stick-slip index (for example, above 0.2) is healthy while drilling. Put another way, torsional oscillations

while drilling help avoid damaging lateral vibrations. An explanation for this coupling could be a mistuning effect. As a

consequence of the changing rpm there is no constant excitation frequency and the system cannot reach a steady state with

high vibration amplitudes.

The right diagram in Figure 6 shows the average ROP versus stick-slip index and peak lateral acceleration. The

relationship to ROP is interesting: there is a zone where higher ROP occurs with low lateral vibration and non-zero SSI (about

0.5). Note that high ROP can also occur with high lateral acceleration for SSI less than 0.2 – but the risk of BHA failure is

higher. An optimal parameter region is around an SSI of 0.5 with most of the 1sRMS lateral vibrations sRMSlata 1, less than

3grms and an average ROP greater than 50ft/h.

Summary and Conclusion Accumulated drilling dynamics data, when combined with context data such as failure reports and environmental information,

can statistically link the stress and load on the BHA system to the probability of MWD tool failure. The most significant stress

variables were identified in a statistical approach using an extensive and rather unique downhole data set and the binary

logistic regression method. Lateral accelerations are by a magnitude more significant than other variables. Other significant

variables are cumulative rotary speed variance and time spent in backward whirl. For lateral vibrations, a table can be

developed which links the probability of failure to time operating above a vibration level threshold. The probability of failure

is also directly related to the cumulative rotary speed variance in revolutions and the time spent in backward whirl.

Based on these charts it is possible to assess the risk or probability of a tool failure for an estimated future vibration load

and thus to assess the likelihood of tool failure under the current, or different, operating conditions. Therefore, by real time

monitoring of these dynamic stress variables it is possible to adjust drilling parameters in a timely manner to reduce the

vibration load on the BHA system, thereby extending BHA life and reducing the probability of a BHA failure.

The analysis also provides an interesting perspective on the development of MWD tools. To date, stick-slip has been an

important consideration in MWD tool condition monitoring, and has been used as an indicator for out-of-specification

operations. This study finds, however, that MWD companies have addressed, and continue to address, the torsional capability

of their tools and this indicator is now of significance only for the slimmer tool size.

The limitations of the study should be clearly understood: it has attempted to quantify only the long term condition

monitoring of MWD Bottom Hole Assemblies. The various criteria studied which are found not to be significant for MWD

BHA condition monitoring may be significant for other purposes. As examples, stick-slip monitoring is significant in drilling

optimization studies; backward rotation is significant in drill bit life assessment. In addition, an MWD tool may still fail due to

a catastrophic load applied over a very short time interval (an impact), such as is caused by jarring on a stuck BHA where the

MWD tool is above the stuck point.

The paper concluded with an analysis of the dependency of lateral vibrations, stick-slip index and ROP, which lead to

conclusions for optimal drilling performance. The best rates of penetration are reached for a stick-slip index less than 0.2, but

with potentially damaging lateral vibrations. An optimal stick-slip index is about 0.5, which has an average ROP greater than

50ft/h, and significantly lower lateral accelerations.

Acknowledgements The authors would like to thank Baker Hughes for their support and permission to publish this paper. They would also like to

thank all the field and application engineers for their help in gathering data for this analysis.

Nomenclature

peaklata , , sRMSlata 1, Lateral peak and 1sRMS acceleration

peakaxa , , sRMSaxa 1, Axial peak and 1sRMS acceleration

peaka tan, , sRMSla 1tan, Tangential peak and 1sRMS acceleration

maxω , minω , avgω maximum, minimum and 5 second average rotary speed

avgβ 5 second average bending rate

avgT 5 second average downhole torque

avgaxF , 5 second average downhole axial force

avgB 5 second average bending moment

10 IADC/SPE 127413

rsvC cumulative rotary speed variance

t∆ sample time

E Energy value

Ω Whirl rate

SSI Stick-slip index

Z Probability of success

f(Z) Probability function

p Significance value

ROP Rate of penetration

References

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