Quest for quality data

Auteur(s) :

Theys, Philippe P. Découvrir l'auteur

Résumé

Cet ouvrage expose l'importance de l'acquisition des données et l'interprétation des processus pour les ingénieurs de terrain travaillant dans le domaine pétrolifère.

Éditeur(s)
- Éd. Technip
Date
- 2011
Notes
- Notes bibliogr. Glossaire
Langues
- Anglais
Description matérielle
- 1 vol. (XXII-248 p.) : ill., couv. ill. en coul. ; 24 cm
Sujet(s)
ISBN
- 978-2-7108-0964-7
Indice
- 665 Industrie pétrolière, pétrochimie
Quatrième de couverture
- The Digital Oilfield is the buzzword of the oil industry in these early years of the 21^st century.
  Data swiftly flows to databases, moves around the world at the speed of light and can be exchanged seamlessly between all stakeholders. From time to time, for good housekeeping, data is cleansed. Many people are involved in these intermediate or final steps.
  But, who cares about the real sources of oilfield data, seismic profiles, wireline and LWD logs, drilling data and core measurements ? This book, Quest for quality data, expresses the real concerns about input data. It explains the inherent weaknesses of the oilfield data acquisition processes and gives recommendations on how to improve them. This quest goes through the paths of uncertainty management and elucidates the important role of the field engineers.
Tables des matières
- - Quest for quality data
  - Philippe P. Theys
  - Technip
  - AcknowledgementsV
  - ForewordVII
  - PrefaceXXI
  - Chapitre 1
    Introduction
  - Part 1
    Why measurements differ from reality
  - Chapitre 2
    Setting the problem with simple examples
  - 2.1 Measurements and reality 5
  - 2.2 Measuring the length of a stick 5
  - 2.3 Measuring the weight of a person with a bathroom scale 6
  - 2.3.1 A minimalist mechanics course on how a bathroom scale works8
  - 2.3.2 Sensitivity of the measurements to parameters9
  - 2.3.3 The pleasing bathroom scale10
  - 2.4 Combining measurements and how a calculator can be deceiving 11
  - 2.5 Comparison with oil industry measurements 11
  - 2.5.1 Unfounded feeling of accuracy and precision on derived properties12
  - 2.5.2 Difference between rock properties and logging measurements13
  - 2.6 Summary 13
  - References 14
  - Chapitre 3
    All well measurements are indirect
  - 3.1 A brief description of the logging process 15
  - 3.2 Spontaneous potential 15
  - 3.3 Resistivity 16
  - 3.3.1 Early tools16
  - 3.3.2 Recent tools18
  - 3.3.3 Tri-axial resistivities20
  - 3.3.4 Exploiting the difference of response between resistivity tools20
  - 3.4 Gamma ray 21
  - 3.5 Density 22
  - 3.5.1 Electron density22
  - 3.5.2 Density23
  - 3.6 Compressional sonic 23
  - 3.7 Other sonic information 24
  - 3.8 Neutron logging 24
  - 3.9 Recent technology 26
  - 3.10 Summary 26
  - References 27
  - Chapitre 4
    Logging measurements do not focus on zones of interest
  - 4.1 Volumes investigated by logging tools 29
  - 4.1.1 Open-hole conditions29
  - 4.1.2 Cased-hole conditions30
  - 4.1.3 Volumes of investigation of logging tools : some numbers32
  - 4.1.4 Turning high-resolution as an advantage33
  - 4.2 Environmental effects 35
  - 4.2.1 Borehole effects35
  - 4.2.2 Mud cake36
  - 4.2.3 Invasion36
  - 4.2.4 Shoulder beds and formation dip37
  - 4.3 Modeling the environment 38
  - 4.3.1 Borehole modeling38
  - 4.3.2 Mud cake modeling : spine and ribs39
  - 4.3.3 Invasion modeling39
  - 4.4 Limitations of environmental modeling 40
  - 4.5 Summary 41
  - References 41
  - Chapitre 5
    Measurements are imprecise and inaccurate
  - 5.1 Elements of reality 43
  - 5.2 Some definitions 43
  - 5.2.1 Academic definitions43
  - 5.2.2 Practical considerations on errors44
  - 5.2.3 Addition of errors45
  - 5.3 What affects precision 46
  - 5.3.1 Logging speed and sampling rate46
  - 5.3.2 Filtering47
  - 5.3.3 Technology47
  - 5.4 What affects accuracy 48
  - 5.4.1 Tool response48
  - 5.4.2 Tool calibration48
  - 5.4.3 Environmental corrections and environmental models48
  - 5.4.4 Environmental effects49
  - 5.5 Repeated measurements 49
  - 5.5.1 Historical importance of repeated data acquisition50
  - 5.5.2 Repeated measurements as an epiphany50
  - 5.5.3 Repeat sections50
  - 5.5.4 Potential issues with repeat sections50
  - 5.5.5 Simultaneous measurements51
  - 5.6 Reproducibility 52
  - 5.6.1 Historical perspective52
  - 5.6.2 Reproducibility tests53
  - 5.6.3 Recent reproducibility studies55
  - 5.7 What may go wrong with a measurement 55
  - 5.8 Summary 55
  - References 56
  - Chapitre 6
    How measurements can suffer from human bias
  - 6.1 Introduction 57
  - 6.2 Biased data 58
  - 6.2.1 Examples58
  - 6.2.2 Where can data bias be introduced ?58
  - 6.3 Are data acquisition companies service companies ? 59
  - 6.3.1 Attributes of a service activity59
  - 6.3.2 Zero defect and zero pain60
  - 6.3.3 Bringing bad news, an important and difficult duty of the logging company61
  - 6.3.4 Historical shift from product quality to service quality62
  - 6.4 Overemphasis on service quality has detrimental effects on oil companies 62
  - 6.4.1 Bias on reporting problems62
  - 6.4.2 Selecting the most pleasant information63
  - 6.4.3 Oil companies may destroy the ability to get correct data63
  - 6.4.4 Satisfying several oil companies may be ultimately impossible63
  - 6.4.5 Satisfying some requests from an oil company may be illegal64
  - 6.5 Long term use of data 64
  - 6.5.1 An example of a century-old trouble-maker64
  - 6.6 Summary 65
  - References 65
  - Chapitre 7
    Complexity
  - 7.1 Historical summary 68
  - 7.1.1 From Colonel Drake to 198068
  - 7.1.2 From the 1980s68
  - 7.2 Borehole trajectory and shape 69
  - 7.2.1 Horizontal wells69
  - 7.2.2 3-D trajectories69
  - 7.2.3 Multilateral wells70
  - 7.2.4 Large holes70
  - 7.2.5 Invasion patterns71
  - 7.2.6 Movement of logging tools and drilling assemblies71
  - 7.3 Mud composition 72
  - 7.3.1 Formate Cesium72
  - 7.3.2 Petrofree Mud72
  - 7.3.3 Other muds72
  - 7.4 Complexity induced by combined measurements 73
  - 7.5 Complexity induced by the hole shape in the logging-while-drilling mode 74
  - 7.6 Incidence of complexity on data acquisition 74
  - 7.7 Summary 74
  - References 75
  - Chapitre 8
    Complication
  - 8.1 Increased complication 77
  - 8.2 Complication in delivery 78
  - 8.2.1 Simple beginnings78
  - 8.2.2 Transition to digital data78
  - 8.2.3 21^st century deliverables79
  - 8.3 Multiplicity of formats 79
  - 8.3.1 Setting the problem79
  - 8.3.2 RP 66/DLIS80
  - 8.3.3 LAS80
  - 8.3.4 Other formats80
  - 8.3.5 Graphical formats81
  - 8.3.6 Other digital platforms81
  - 8.4 Content of the digital records 81
  - 8.4.1 Classification of data objects82
  - 8.4.2 First example : the gamma ray log83
  - 8.4.3 Second example : a modern combination of measurements83
  - 8.4.4 Composite logs84
  - 8.4.5 Variability in the volume of delivery85
  - 8.5 Additional issues related to data content 86
  - 8.5.1 Further division : Real-time and memory mode86
  - 8.5.2 Several versions of a job may exist86
  - 8.5.3 Changes of formation parameters with time86
  - 8.5.4 The seven dimensions of a data object88
  - 8.6 Sophistication in measuring tools 88
  - 8.7 Summary 89
  - References 89
  - Chapitre 9
    Wysinwytii
  - 9.1 Early permeability curve 91
  - 9.2 Log headers 91
  - 9.3 Depth 93
  - 9.4 Volume integration 93
  - 9.5 Calibrations 95
  - 9.5.1 Caliper calibration case study96
  - 9.6 Remarks 98
  - 9.7 Tool sketch 99
  - 9.8 Validated and complete information 100
  - 9.8.1 Annotations101
  - 9.9 Summary 102
  - References 102
  - Chapitre 10
    Misconceptions
  - 10.1 The supremacy of real time and of short term 103
  - 10.2 Normalization 103
  - 10.2.1 Example of normalization104
  - 10.3 Data cleansing 104
  - 10.4 Interpretation as data quality control 105
  - 10.4.1 Example 1 of erroneous data that can be interpreted105
  - 10.4.2 Example 2 of erroneous data that can be interpreted105
  - 10.5 The recovery factor 106
  - 10.6 Summary 106
  - References 106
  - Part 2
    Quest for quality data
  - Chapitre 11
    The different uses of logging data
  - 11.1 Data and decisions 109
  - 11.1.1 Medical example : control of cholesterol content in blood110
  - 11.1.2 Example of decisions taken with a bathroom weighting scale110
  - 11.1.3 Example of decision process with geoscience data111
  - 11.1.4 Increased challenge in log interpretation112
  - 11.2 Role of logging data 113
  - 11.2.1 Depth correlations113
  - 11.2.2 Quantitative reserve evaluation at an early field stage116
  - 11.2.3 Enhanced recovery116
  - 11.2.4 Field study116
  - 11.2.5 Unitization and redetermination117
  - 11.2.6 Example of multiple use of data117
  - 11.3 Value of data 118
  - 11.3.1 Invisible data loss120
  - 11.4 Summary 120
  - References 121
  - Chapitre 12
    Brochure specifications
  - 12.1 Importance of specifications 123
  - 12.2 Specifications proposed by the data vendors 124
  - 12.2.1 Strong emphasis on operating specifications124
  - 12.2.2 Suspiciously similar specifications from different vendors125
  - 12.2.3 Hyperbole125
  - 12.2.4 Minimum specifications126
  - 12.3 Underlying definitions of the specifications 126
  - 12.3.1 Precision126
  - 12.3.2 Vertical resolution127
  - 12.3.3 Depth of investigation127
  - 12.3.4 Conditions of applications of the specifications127
  - 12.3.5 Testing the specifications in the real world127
  - 12.4 Getting accuracy specifications 127
  - 12.4.1 Quantifying systematic errors127
  - 12.4.2 The challenge of improved technology128
  - 12.5 Obtaining precision specifications 129
  - 12.6 Developing reproducibility specifications 129
  - 12.7 Examples of complete specifications 129
  - 12.7.1 Accuracy and precision of density measurements129
  - 12.7.2 Vertical resolution of a resistivity tool130
  - 12.7.3 Depth of investigation of a resistivity tool130
  - 12.8 Additional measurement information 131
  - 12.8.1 Planning tables131
  - 12.9 A first look at uncertainties 132
  - 12.10 Planning of a logging job with specifications 133
  - 12.11 Summary 135
  - References 135
  - Chapitre 13
    Quest for uncertainties : from brochure specifications to real uncertainties
  - 13.1 Starting from vendors specifications 137
  - 13.2 Real uncertainties 138
  - 13.2.1 Uncertainties reported by data vendors138
  - 13.2.2 Uncertainties observed when multiple passes are available139
  - 13.3 Defining homogeneous beds 141
  - 13.4 Estimating random errors for uncertainty analysis 142
  - 13.4.1 From vendor specification to actual well conditions142
  - 13.4.2 Using a different logging speed (rate of penetration) or a different sampling rate from the one used in the vendor's specification142
  - 13.4.3 Using a different signal processing method143
  - 13.4.4 Using a different (...)143
  - 13.4.5 Using a different value than the reference given by the vendor143
  - 13.4.6 Example of computation of the precision for the density144
  - 13.4.7 Taking into account the thickness of the zones of interest145
  - 13.5 Handling systematic errors 146
  - 13.5.1 Quantifying propagated errors147
  - 13.5.2 Integrating tool drift in the uncertainty149
  - 13.6 One step further : Anticipating other sources of uncertainty 150
  - 13.6.1 Density measurement150
  - 13.6.2 Density uncertainty due to the hole diameter151
  - 13.6.3 Uncertainty originating from (...)151
  - 13.6.4 Uncertainty due to the hole rugosity151
  - 13.6.5 Combining uncertainties151
  - 13.6.6 Uncertainty due to a depth-matching error152
  - 13.7 Visualization of uncertainties 152
  - 13.8 Summary 153
  - References 153
  - Chapitre 14
    Deliverables
  - 14.1 Importance of data completeness 155
  - 14.1.1 Who defines the deliverables ?155
  - 14.1.2 The need for graphical displays156
  - 14.1.3 Standardization156
  - 14.1.4 Format and content156
  - 14.2 Content of the graphical files 159
  - 14.2.1 Depth-related information160
  - 14.2.2 Tool sketch160
  - 14.2.3 Remarks161
  - 14.2.4 Job chronology162
  - 14.2.5 Parameter summary and parameter change163
  - 14.2.6 Raw and QC curves163
  - 14.2.7 LQC stamp165
  - 14.3 Digital files 165
  - 14.3.1 The basic digital file165
  - 14.3.2 Raw data on digital files165
  - 14.3.3 Time based data165
  - 14.3.4 Data in digital files167
  - 14.4 The importance of contextual information 168
  - 14.5 Summary 169
  - References 169
  - Chapitre 15
    Depth
  - 15.1 Importance of depth 171
  - 15.1.1 Examples of challenges proposed by depth measurements171
  - 15.2 The different depths 172
  - 15.2.1 Wireline mark-derived depth172
  - 15.2.2 Wireline calibrated-wheel depth173
  - 15.2.3 Driller's and LWD depth174
  - 15.2.4 Expected differences between the measured depths175
  - 15.3 Importance of getting it right at surface 175
  - 15.3.1 Datums176
  - 15.3.2 Clear definition of the North reference177
  - 15.3.3 Elevation model177
  - 15.4 The depth information box 178
  - 15.4.1 General178
  - 15.4.2 Example: Schlumberger179
  - 15.4.3 Depth policy documentation and traceability179
  - 15.5 Reconciliation of depths 180
  - 15.6 Wireline creep 181
  - 15.6.1 Definition of wireline creep181
  - 15.6.2 Causes of creep181
  - 15.6.3 Modeling creep181
  - 15.6.4 Confirmation of creep182
  - 15.6.5 Practical examples182
  - 15.6.6 Effects in cased-hole182
  - 15.7 Summary 183
  - References 184
  - Chapitre 16
    Hidden treasures
  - 16.1 Chart books 185
  - 16.1.1 Old charts185
  - 16.2 Job planners 186
  - 16.3 Quality Control curves 188
  - 16.4 Details on calibrations 191
  - 16.4.1 Calibration guides191
  - 16.4.2 Detailed information on calibration193
  - 16.4.3 Successive calibrations194
  - 16.5 Information on wear 194
  - 16.6 Summary 195
  - References 197
  - Chapitre 17
    Contribution of the field engineer to the quality of data
  - 17.1 Historical context 199
  - 17.1.1 Early field engineers199
  - 17.1.2 The field engineer in the future201
  - 17.1.3 An intermediate step - Remote support and control202
  - 17.2 The human factor 202
  - 17.2.1 Motivation and performance203
  - 17.3 The multiple functions of the field engineer 204
  - 17.3.1 A diplomat204
  - 17.3.2 A safety manager204
  - 17.3.3 An experimentalist, a troubleshooter and an entrepreneur205
  - 17.3.4 A physicist205
  - 17.3.5 A reporter205
  - 17.3.6 The guardian of data205
  - 17.3.7 A decision maker205
  - 17.4 Human error management 206
  - 17.5 Blunder management 206
  - 17.5.1 The field engineer and the computer206
  - 17.5.2 Tasks managed by computer technology206
  - 17.5.3 Duties of the engineer207
  - 17.6 Integrity or handling voluntary human errors 208
  - 17.6.1 The field engineer oath208
  - 17.6.2 Reinforcement in logging companies208
  - 17.7 Emphasis on data 209
  - 17.7.1 A log is not only the main pass209
  - 17.8 Summary 210
  - References 210
  - Chapitre 18
    Drilling data
  - 18.1 Drilling data, also 211
  - 18.2 Historical context 211
  - 18.2.1 Surface measurements211
  - 18.2.2 Enter MWD212
  - 18.2.3 Drilling isn't getting easier213
  - 18.3 The specific nature of drilling data 213
  - 18.3.1 What is measured and observed213
  - 18.3.2 The time domain213
  - 18.3.3 Lack of standards214
  - 18.3.4 Quality control and quality assurance215
  - 18.4 Data processing 218
  - 18.4.1 Example of incorrect processing219
  - 18.4.2 Sampling, processing and statistics220
  - 18.4.3 Graphical data presentation221
  - 18.5 Cost of inferior quality 221
  - 18.5.1 Example of an incorrect decision as a result of poor fluid data and inadequate data flow222
  - 18.5.2 Non-productive time (NPT) and Invisible lost time (ILT)223
  - 18.6 Improving communication 223
  - 18.7 Drilling data in the future 224
  - 18.8 Summary 224
  - References 225
  - Chapitre 19
    Coring data
  - 19.1 Measurements on cores 227
  - 19.1.1 Multiple measurements on core plugs227
  - 19.1.2 Additional issues on core plugs228
  - 19.2 Combining data from different sources 228
  - 19.3 Summary 229
  - References 229
  - Chapitre 20
    Conclusions and recommendations
  - 20.1 Difference between real and measured values 232
  - 20.1.1 No misnomer232
  - 20.1.2 Start with a higher profile for measurement specifications232
  - 20.1.3 The path to errors and uncertainties232
  - 20.2 Standardization 233
  - 20.2.1 Similar structure of deliverables233
  - 20.2.2 No danger of commoditization233
  - 20.3 The logger's oath 233
  - 20.4 Improved databases 234
  - 20.5 Documentation 234
  - 20.6 The required evolution of interpretation software 235
  - 20.7 Beginning of the journey 235
  - References 235
  - Appendix
  - Appendix 1 : Quantifying the level of proficiency in data quality 237
  - Appendix 2 : Deliverables 239
  - Appendix 3 : Lexicon 240
  - Appendix 4 : Metrological definitions 241
  - Appendix 5 : Log Quality Control checklist 243
  - Appendix 6 : Advanced computation of uncertainties for the density log 245
  - Appendix 7 : Quest for mnemonics 248
Origine de la notice:
- FR-751131015