Fatigue in Oil and Gas



Ranjana Mehta, Ph.D. Assistant Professor Environmental and Occupational Health Director, NeuroErgonomics Lab Faculty, Texas A&M Institute for Neuroscience S. Camille Peres, Ph.D. Assistant Professor Environmental and Occupational Health Director, RIHM Lab

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Today  Fatalities and Injuries in Oil and Gas  What does “fatigue” mean to this industry  Barriers to fatigue monitoring and management  Our current industry-academic research efforts  What’s next?

Mehta, RK (2016)

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Occupational fatalities in the Oil and Gas Industry Fatality rate in the Oil and Gas industry is

EIGHT times that for all U.S. workers Mehta, RK (2016)

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Occupational Fatalities, Oil and Gas Extraction Industry, 2003-2013 Fatality Rate (Oil and Gas)

160

35

140

30

# of Deaths

120

25

100

20

80 15

60

10

40 20

5

0

0 2006

Mehta, RK (2016)

2007

2008

2009

2010

2011

Source: BLS CFOI/QCEW (2013). Rate per 100,000 workers per year

2012

2013

Fatality rate (/100,000 workers)

# of Deaths (Oil and Gas) Fatality Rate (All industries)

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Fatality Characteristics, 2003-2012

Source: BLS CFOI/QCEW (2013). Rate per 100,000 workers per year. Includes NAICS 211, 213111, 213112. Mehta, RK (2016)

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What is Fatigue?  “A physiological state of reduced mental or physical

performance capability resulting from sleep loss or extended wakefulness, circadian phase, or workload (mental and/or physical activity) that can impair a crew member’s alertness and ability to safely operate an aircraft or perform safety related duties” - International Civil Aviation Organization

… to the Oil and Gas Industry? Mehta, RK (2016)

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Fatigue: Critical Risk Factor in Oil and Gas

Mehta, RK (2016)

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Fatigue: Cause and Consequence   

 

Causes Extended wakefulness: 12-hour shifts Long durations of hitch: 14- to 28-days hitch Physical and cognitive demands Distinct psychosocial stressors Environmental hazards (heat/cold stress, noise, chemicals, machinery)

Mehta, RK (2016)

Operational performance decrements

FATIGUE

Negative Health outcomes

Accidents/Fatalities

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Current Fatigue MANAGEMENT Practices in the U.S. Oil and Gas Industry  Fatigue Management Practices examples:  Training

 In-Vehicle Monitoring Systems (IVMS)  Journey Management  Shift-change during hitch

 Addressing transportation-related fatalities

identified through BLS fatalities reports  Some may have unintended consequences!

 FRMS: API 755

Mehta, RK (2016)

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Measuring fatigue No ONE definition of fatigue  An academic’s perspective  Controlled environment (often laboratories)  Detailed (and sometimes intrusive) measurements that offer high precision  Lack operational relevance Translational research

Mehta, RK (2016)

 A practitioner’s perspective  Unobtrusive and feasible  Relevant to operational goals

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Fatigue Assessment Methods

Mehta, RK (2016)

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Barriers  To fatigue monitoring  Inherently hazardous environments: barrier to objective fatigue assessment (“intrinsically safer” methods needed)  No systematic investigation on how the different risk factors interact  Major focus on sleep and shift-work  Undermining the impacts of physical and cognitive

overload during hitch

 To fatigue management  Lack of evidence-based practices!  Lack of resources: staffing issues, time between shifts Mehta, RK (2016)

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Our Industry-Academic Partnership  Is a Research to Practice (r2P) effort to:  Improve our understanding of the relationships between hazardous offshore working conditions and fatigue-related injuries /accidents  Facilitate improved fatigue evaluation practices in this industry through the development of a “field-friendly” fatigue assessment inventory  To better inform fatigue management practices  Initial effort:  Compare Physiological and Self-report measures Mehta, RK (2016)

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Physiological responses from workers  Collected sensor data from 10 workers over 15 days  Current analyses on HR and ambulation data processed by software from vendor  Participants (n=10)  Not complete days from all operators (6-15 days range, with day 1 of incomplete data)  6 operators had shift changes (swing shifts)  Only 3 had short changes before the swing shifts  Nature of short change not consistent

 The 3 non-short change operators had 24 hour break between the

swing shifts

Mehta, RK (2016)

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Physiological Measures 

Currently in the data analyses phase

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Monitoring offshore workers over three weeks, across different job categories, through: 

Sensors, measuring  ECG; Heart rate; R-R interval (256 Hz)  Respiratory rate;

 Skin temperature;  Accelerometer X,Y,Z  Body position; Motion status; Fall alert  Device alarms; Subject alerts



Mehta, RK (2016)

The only clinically validated INTRINSICALLY SAFER sensors on the market!

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Work Schedules Operator Day

1

2

3

4

5

6

7

8

12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM

Mehta, RK (2016)

1 15

2 14

3 15

4 14

5 15

6 11

7 6

8 15

9 15

10 8

Operator Day

19

9

20

10

21

11

22

12

23

13

24

14

25

15

26

16

12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM 6:00 AM 12:00 PM 6:00 PM

1 15

2 14

3 15

4 14

5 15

6 11

7 6

8 15

9 15

Operators with shift changes Regular shifts Short changes Swing shifts Rest

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Issues encountered and Initial Approach 

Issues encountered  Sensor belts, sensor type, attrition  Large data with noise, small sample size without multiple N from each

potential factor (job type, shift time, shift type)

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Initial Approach  Within and Between variability in job demands  Did not monitor work output from each operator  Used ambulation data from sensor to quantify worker HR levels while STATIONARY and AMBULATORY  Focus on heart rate (HR)  Sensitive to fatigue due to several sources (sleep, shiftwork, physical and cognitive workload)  However, HR profiles for exposure assessment in field not available

Mehta, RK (2016)

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HR Analyses 

HR Creep - > Indicator of fatigue during uniform work 

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For non uniform work, stabilized HR (~4 mins) can be used as a surrogate for “uniform work bouts”

For this study, we identified ~4 min bouts of time where 

HR did not change ±5 bpm 

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We obtained:  

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Across both Stationary and Ambulatory activities

# of stabilized bouts identified (frequencies) Min, Avg, and Max HR for each bout

Plans are underway to investigate other aspects of HR data (e.g., heart rate variability)

Mehta, RK (2016)

max

HR

rest rest

work

rest

work

rest

work

rest

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HR over days ~19-72% increase in HR during ambulatory work bouts HR did not vary over time (however, this was not consistent across ALL operators) Activity x Day

Avg HR (bpm)

200

150

100

50

0 1

2

3

4

5

6

7

8

9

10

11

Day Stationary HR

Mehta, RK (2016)

Ambulatory HR

Pooled across all operators and shift times/types Activity (P