Production Availability and Reliability: Use in the Oil and Gas industry

The objective of the book is to provide all the elements to evaluate the performance of production availability and reliability of a system, to integrate them and to manage them in its life cycle. By the examples provided (case studies) the main target audience is that of the petroleum industries (where I spent most of my professional years). Although the greatest rigor is applied in the presentation, and justification, concepts, methods and data this book is geared towards the user.

Table of Contents:
Preface xv Chapter 1. Basic Concepts 1 1.1. Introduction 1 1.2. Definition of terms 1 1.2.1. Risk 1 1.2.2. Time definitions 2 1.2.3. Failures and repairs 4 1.2.4. IEC 61508 terms 8 1.3. Definition of parameters 10 1.3.1. Reliability 10 1.3.2. Maintainability 12 1.3.3. Availability and production availability 12 1.3.4. Dependability 13 1.3.5. Definitions used by maintenance engineers 13 1.3.6. Definitions used in the refinery industry 14 1.4. The exponential law/the constant failure rate 14 1.4.1. Reliability 14 1.4.2. Validity 15 1.4.3. Oil and gas industry 16 1.5. The bathtub curve 16 1.5.1. Meaning 16 1.5.2. Useful life and mission life 18 1.5.3. Validity 18 1.5.4. Oil and gas industry 18 Chapter 2. Mathematics for Reliability 21 2.1. Introduction 21 2.2. Basis of probability and statistics 22 2.2.1. Boolean algebra 22 2.2.2. Probability relations 22 2.2.3. Probability distributions 24 2.2.4. Characteristics of probability distributions 24 2.2.5. Families and conjugates 26 2.3. Formulae and theorems 27 2.3.1. Combinatorial analysis 27 2.3.2. Central limit theorem 28 2.3.3. Chebyshev’s inequality 28 2.3.4. Laws of large numbers 28 2.3.5. Supporting functions and distributions 29 2.3.6. Bayes’ theorem 30 2.4. Useful discrete probability distributions 32 2.4.1. Binomial distribution 33 2.4.2. Poisson distribution. 33 2.5. Useful continuous probability distributions 35 2.5.1. Exponential distribution 35 2.5.2. Uniform distribution 36 2.5.3. Triangular distribution 37 2.5.4. Normal distribution 38 2.5.5. Log-normal distribution 40 2.5.6. Weibull distribution 43 2.5.7. Gamma distribution 44 2.5.8. Beta distribution 45 2.5.9. Chi-squared distribution 46 2.5.10. Fisher-Snedecor distribution 46 2.6. Statistical estimates 47 2.6.1. Estimates 47 2.6.2. Calculation of point estimate 47 2.6.3. Calculation of confidence interval 50 2.6.4. Heterogeneous samples 52 2.6.5. Implementation 53 2.7. Fitting of failure distribution 53 2.7.1. Principle 53 2.7.2. Median rank method 54 2.7.3. Implementation 55 2.8. Hypothesis testing 57 2.8.1. Principle 57 2.8.2. Existing tests. 58 2.8.3. Implementation 58 2.9. Bayesian reliability 60 2.9.1. Definition 60 2.9.2. Use of Bayes’ theorem 61 2.9.3. Bayesian inference 61 2.9.4. Selection of the prior probability distribution 62 2.9.5. Determination of the posterior probability distribution 62 2.9.6. Bayesian credibility interval 64 2.10. Extreme value probability distributions 65 2.10.1. Meaning. 65 2.10.2. The three extreme value probability distributions 65 2.10.3. Use in the industry 66 Chapter 3. Assessment of Standard Systems 67 3.1. Introduction 67 3.2. Single item 67 3.2.1. Availability 68 3.2.2. Number of failures 69 3.3. System reliability 70 3.3.1. Series systems 70 3.3.2. Parallel systems 72 3.4. Specific architectures 73 3.4.1. Method of analysis 73 3.4.2. Redundant item system 74 3.5. On-guard items 76 3.5.1. Unrevealed failures 76 3.5.2. Full formula 77 3.5.3. Optimum proof test duration 79 Chapter 4. Classic Methods 81 4.1. Introduction 81 4.2. Failure Mode and Effects Analysis 81 4.2.1. Conventional Failure Mode and Effects Analysis/Failure Mode,Effects and Criticality Analysis 81 4.2.2. Functional/hardware FMEA 84 4.2.3. Case study 84 4.3. Fault trees 89 4.3.1. Conventional fault trees 89 4.3.2. Fault tree extensions 93 4.3.3. Facilities provided by software packages 94 4.3.4. Case study 94 4.4. Reliability block diagrams 98 4.4.1. Conventional RBDs 98 4.4.2. RBD extension 102 4.4.3. Facilities provided by software packages 103 4.4.4. Case study 103 4.5. Monte Carlo method 104 4.5.1. Principle 104 4.5.2. Use for production availability and reliability 106 4.5.3. How many runs are enough? 107 Chapter 5. Petri Net Method 109 5.1. Introduction 109 5.2. Petri nets 110 5.2.1. Definition 110 5.2.2. Mathematical properties 111 5.2.3. Petri net construction 112 5.2.4. GRAFCET 117 5.3. IEC 62551 extensions 117 5.3.1. Extensions to structure 117 5.3.2. Modified execution rules 120 5.4. Additional extensions 121 5.4.1. Extensions to structure 121 5.4.2. Modified execution rules 122 5.5. Facilities provided by software packages 123 5.5.1. Additional extensions to structure 123 5.5.2. Modified execution rules 123 5.5.3. Petri net processing 123 5.5.4. Results 123 5.6. Petri net construction 124 5.6.1. Petri net modeling 124 5.6.2. Minimizing the risk of error input 124 5.6.3. Petri net checking 124 5.6.4. Petri net validation 125 5.7. Case study 125 5.7.1. System description 125 5.7.2. Petri net model 126 Chapter 6. Sources of Reliability Data 133 6.1. Introduction 133 6.2. The OREDA project 133 6.2.1. History 133 6.2.2. Project management and organization 135 6.2.3. Description of OREDA 2015 handbooks 135 6.2.4. Use of the data tables 137 6.2.5. Use of the additional tables 141 6.2.6. Reliability database and data analysis software 143 6.2.7. Data collection software 144 6.3. The PDS handbook 144 6.3.1. History 144 6.3.2. Description of the handbook 145 6.3.3. Use of the handbook 145 6.4. Reliability Analysis Center/Reliability Information Analysis Center publications 145 6.4.1. History 145 6.4.2. Non-electronic Part Reliability Data handbook 146 6.4.3. FMD 146 6.4.4. NONOP 146 6.4.5. Use of the publications 146 6.5. Other publications 147 6.5.1. EXIDA handbooks 147 6.5.2. Electrical items 147 6.5.3. Pipelines 148 6.5.4. Flexibles 149 6.5.5. Miscellaneous 149 6.6. Missing information 150 Chapter 7. Use of Reliability Test and Field Data 151 7.1. Introduction 151 7.2. Reliability test data 151 7.2.1. Principle 151 7.2.2. Test organization 152 7.2.3. Assessment of failure rate 152 7.3. Field data 154 7.3.1. Principle 154 7.3.2. Data collection organization 155 7.3.3. Assessment of failure rate 155 7.3.4. Assessment of probability to fail upon demand 156 7.3.5. Assessment of MRT 156 7.3.6. Case study 156 7.4. Accelerated tests 157 7.4.1. Principle 157 7.4.2. Example 158 7.4.3. Highly accelerated tests 159 7.5. Reliability growth 159 7.5.1. Principle 159 7.5.2. Main models 159 Chapter 8. Use of Expert Judgment. 163 8.1. Introduction 163 8.2. Basis 164 8.2.1. Definitions 164 8.2.2. Protocol for expert elicitation 164 8.2.3. Role of the facilitator 165 8.3. Characteristics of the experts 166 8.3.1. Definition 166 8.3.2. Selection 166 8.3.3. Biases 167 8.3.4. Expert weighting 168 8.3.5. Expert dependence 169 8.3.6. Aggregation of judgments 169 8.4. Use of questionnaires 169 8.4.1. Conditions of use 169 8.4.2. The Delphi method 170 8.4.3. Case study 171 8.5. Use of interactive group 173 8.5.1. Number of experts 173 8.5.2. Procedure. 173 8.6. Use of individual interviews 174 8.6.1. Conditions of use 174 8.6.2. Case study 174 8.7. Bayesian aggregation of judgment 175 8.7.1. Form of information provided by experts 175 8.7.2. Assessment of failure rate (or MTBF) 176 8.7.3. Assessment of probability of failure upon demand 177 8.8. Validity of expert judgment 177 Chapter 9. Supporting Topics 179 9.1. Introduction 179 9.2. Common cause failures 179 9.2.1. Introduction 179 9.2.2. Definition 180 9.2.3. Defenses against CCF 181 9.2.4. CCF modeling with the beta-factor method 182 9.2.5. CCF modeling with the shock method 185 9.2.6. Extension of the beta-factor model: the PDS method 188 9.2.7. Field data 189 9.2.8. Impact of CCF on system reliability 190 9.2.9. Impact of testing policy on CCF 191 9.2.10. Impact of CCF on system production availability 194 9.2.11. Benchmark on CCF assessment 194 9.3. Mechanical reliability 195 9.3.1. Characteristics 195 9.3.2. Stress-strength interference 195 9.3.3. Empirical reliability relationships 197 9.3.4. Comparison with system (constant failure rate) approach 199 9.4. Reliability of electronic items 199 9.4.1. Characteristics 199 9.4.2. MIL-HDBK-217 200 9.4.3. UTE-C-80811 201 9.4.4. Other reliability data books 201 9.4.5. EPRD 203 9.4.6. Effect of dormancy period 203 9.4.7. Common cause failures 203 9.4.8. Comparison of previsions 204 9.4.9. Use in the oil and gas industry 205 9.5. Human reliability 205 9.5.1. Human factors 205 9.5.2. Human reliability in the nuclear industry 205 9.5.3. Evaluation of HRA techniques 206 9.5.4. Human reliability in the oil and gas industry 206 Chapter 10. System Reliability Assessment 209 10.1. Introduction 209 10.2. Definition of reliability target 209 10.2.1. Absolute reliability target 209 10.2.2. Risk target 210 10.3. Methodology of system reliability study 211 10.3.1. Overall description 211 10.3.2. Step 1: system analysis 212 10.3.3. Step 2: qualitative analysis. 212 10.3.4. Step 3: quantitative data selection 212 10.3.5. Step 4: system reliability modeling 214 10.3.6. Step 5: synthesis 214 10.4. SIL studies 214 10.4.1. Introduction 214 10.4.2. SIL assignment 214 10.4.3. SIL demonstration 217 10.5. Description of the case study 217 10.5.1. Origin of the risk 217 10.5.2. Description of the standard SIF 219 10.5.3. Risk assessment 219 10.6. System analysis 220 10.6.1. Description of HIPS functioning 220 10.7. Qualitative analysis 221 10.7.1. FMEA 221 10.7.2. CCF analysis 223 10.8. Quantitative data selection 225 10.8.1. Selection of reliability data 225 10.8.2. Collection of proof test data 225 10.8.3. CCF quantification 226 10.9. System reliability modeling 226 10.9.1. Building of system reliability model 226 10.9.2. System reliability calculation 226 10.10. Synthesis 232 10.10.1. Conclusions 232 10.10.2. Recommendations 233 10.11. Validity of system reliability assessments 234 10.11.1. Reports 234 10.11.2. Conclusions 234 Chapter 11. Production Availability Assessment 235 11.1. Introduction 235 11.2. Definition of production availability target 235 11.2.1. Absolute production availability target 235 11.2.2. Economic target 235 11.3. Methodology 236 11.3.1. Events considered in production availability assessments 236 11.3.2. Overall description 236 11.3.3. Step 1: system analysis 238 11.3.4. Step 2: quantitative data selection 238 11.3.5. Step 3: production availability assessment 238 11.3.6. Step 4: synthesis 238 11.4. System analysis 239 11.4.1. Determination of system running modes 239 11.4.2. Item failure analysis 242 11.5. Quantitative data selection 244 11.5.1. Selection of reliability data 244 11.5.2. Collection of operational data 245 11.6. Production availability assessment 246 11.6.1. Building of production availability model 246 11.6.2. Production availability calculations 246 11.7. Synthesis 248 11.7.1. Main results 248 11.7.2. Additional economic parameters 249 11.7.3. Flared gas 251 11.7.4. Other results 253 11.7.5. Recommendations 256 11.8. Uncertainty on the reliability parameters 256 11.9. Validity of production availability assessments 257 Chapter 12. Management of Production Availability and Reliability 259 12.1. Introduction 259 12.2. Principles of dependability management 260 12.2.1. Dependability property management 260 12.2.2. Phasing of the management 260 12.2.3. Lifecycle costing and dependability 261 12.3. Technical specifications 262 12.3.1. Contents. 262 12.3.2. Reliability specification 262 12.3.3. Production availability specification 263 12.4. Reliability and production availability program 264 12.4.1. Contents. 264 12.4.2. Reliability program 266 12.4.3. Production availability program 267 12.5. Validation of system reliability 267 12.5.1. Reliability data collection 267 12.5.2. Random failures 268 12.5.3. Common cause failures 268 12.6. Validation of production availability 268 12.6.1. Useful life 268 12.6.2. Reliability data 269 12.6.3. Production data 269 12.6.4. Use of production availability model 269 Appendices 271 Appendix 1. Notations and Abbreviations 273 Appendix 2. Markov Chain 283 Appendix 3. Comparison of Modeling Methods 293 Appendix 4. Solutions of Exercises. 301 Bibliography 315 Index 323

About the Author :
Alain Leroy worked with an engineering firm and an oil and gas company before heading the engineering subsidiary of an insurance group. He created and managed his own consulting company (Fractal Système) up until 2015.