drilling data hub
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Drilling Data Hubfor Real-time Drilling Applications
NorTex Data Science Cluster Workshop, OTC 2018
Fionn Iversen, IRIS
Outline
1.IRIS background
2.Challenges for RT applications
3. Semantic Data Model
4. Example case
5. Signal types
6. DDHub development
7. Conclusions
IRIS
• Key R&D infrastructure• Full-scale test-rig
• OpenLab Drilling simulator (twin)
• History• Nearing two decades of development of
real-time drilling applications
• Automation
• Monitoring
• Advisory
Challenge for real-time applications
Drilling Control System
Measurements
Downhole Measurements
Mud logging
Wired Pipe ASM
Mud properties
✓ Ok for log display
Historian logging
X Difficult to use for realtime calculations
1. Need for dynamic capability - Hardware changes- Accessing the right data at the
right time
2. Correct data input and application
Current data communication• No time synchronization
• Unknown latency
• Data coming in chunks
• Different refresh rates
• Mnemonic jungle
• Unknown origin of measurement
• Unspecified corrections if any
• No information about accuracy
• No information about validity
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Examples1. New data source available
• Scenario: a short time after the mud pumps are started, wired pipe telemetry is brought to life
• New traces are added to the WITSML log object, and it is necessary to perform a manual mapping of the WITSML signals for hydraulics applications to be aware
2. Extraordinary configuration
• Because there is not enough flow to clean the hole, the cement pump is also used to pump in the well• Manual configuration would be required to inform the a hydraulics application that the cement pump is pumping into the
well
3. Choosing the best relevant signal
• An application requires a hook-load measurement from a draw-works hoisting system
• The application shall perform in-slips detection based on hook-load• The application can interpret loads and forces
• If the best suited signal becomes invalid, the application shall switch automatically to using the next best signal
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Data semantic model
▪ Describes the meaning of its instances. ▪ Defines how the stored data relate to the real world.▪ Enables parties to the information exchange to interpret meaning
(semantics) from the instances, without the need to know the meta-model.
• DDHub semantical data model:• Defines precisely the meaning of every signal such that a consumer
application can discover the significance of each signal and correctly choose between signals of similar nature with respect to application.
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Rig Information Model
Acknowledgement Hans-Uwe Brackel, SPE DSATS and BHGE
Objectives
Aggregate real-time information from multiple sources:• To address problems at the “drilling process management level” (not at the detailed machine management level)• To allow consumer applications to perform inferences on drilling real-time signals that are semantically described in the aggregation
server
Machine DAQ#1
Machine DAQ#2
Machine DAQ#3
Machine DAQ#4
Machine DAQ#5
Drilling machine Service #1
Drilling fluid service#2
Downhole Service #3
DD-Hub
Individual machine control and management
Individual service control and management that do not need inter-communication
Drilling process control and management
Drilling process management #1
Drilling process management #2
Example use case
Use case #1• An application requires a hook-load measurement from a hoisting system based on a draw-works• The application is interested in getting the measurement that is taken as close as possible to the top
of drill-string when the string is not in-slips• The application wants to manage itself in-slips detection based on hook-load• The application can both interpret loads and forces• If the best suited signal gets invalid, the application can switch automatically to the next best signal
What kind of semantic model can support this type of inferences?
Value Real-time value
Le
ge
nd
TAG1234: 49800
Realtime value
Quantity: [M]
Meaningful: 10
Value: 49800
Instance of a classInstance of
Value Real-time value
Class Data class
Le
ge
nd
Quantity
Mass
Properties of a Quantity• Physical quantity: undefined• Meaningful precision: undefined• Value
Properties of Mass• Physical quantity: [M](kg)• Meaningful precision: 10
More specialized
More generalized
A class defines the characteristics and behavior of similar entities
An instance of a class is a specific entity
Instance of a classInstance of
Value Real-time value
Class Data class
Le
ge
nd
Quantity
Mass
Properties of a Signal• Time stamps: measured, acquired• Refresh rates: constant, variable• Physical quantity:
Signal
Measurement Estimated Value Set-point Command Reference Parameter
Additional properties of a Measurement• Uncertainty: accuracy and precision• Validity:
Time-Stamp Measured: 20180222 09:57:23.45
Refresh interval: 0.050
Uncertainty: (200, 100)
Valid: True
Quantity: [M]
Meaningful: 10
Value: 49800
Notation simplificationInstance of
Value Real-time value
Class Data class
Le
ge
nd
Time-Stamp Measured: 20180222 09:57:23.45
Refresh interval: 0.050
Uncertainty: (200, 100)
Valid: True
Quantity: Mass
Quantity: [M]
Meaningful: 10
Value: 49800
Or even shorterClass: Measurement
Quantity: Mass
Value: 49800
Quantity
Mass
Signal
Measurement Estimated Value Set-point Command Reference Parameter
Transformation
Force To Load
Signal
Measurement
Transformed Measurement
Direct Measurement
Estimated Value Set-point Command Reference Parameter
Instance ofValue Real-time value
Class Data class
Le
ge
nd
Function Transformation
Transformed to
Input toTransformation
• A transformation specifies the inputs and the output
• A transformation does not do effectively the mathematical operation, instead it describes the relationship between inputs and the output
Quantity
Mass Force
Class: Force To Load
Class: Direct Measurement
Quantity: Force
Value: 488538
Class: Transformed Measurement
Quantity: Mass
Value: 49800
• Some measurements are direct, others are transformed from one or several signals
ParameterInstance of
Value Real-time value
Class Data class
Le
ge
nd
Function Transformation
Transformed to
Input to
Depend upon
• Not all signals are measurements as there can be parameter, i.e. a value that can change but not so often
• A transformation may depend upon some configuration parameters
Signal
Measurement
Transformed Measurement
Direct Measurement
Estimated Value Set-point Command Reference Parameter
Transformation
Force To Load
Quantity
Mass Force Acceleration
Class: Force To Load
Class: Direct Measurement
Quantity: Force
Value: 488538
Class: Transformed Measurement
Quantity: Mass
Value: 49800
Class: Parameter
Quantity:
Acceleration
Value: 9.81
Mechanical element
Top-drive
Top-drive Body
Active Element
ElevatorTop-drive
shaft
Drill-string
top of drill-string
Class: Top-drive body
Class: Top-drive shaft
Status: Connected
Position in a logical circuit
Instance ofValue Real-time value
Class Data class
Le
ge
nd
Function Transformation
Transformed to
Input to
Depend upon
Logical elementState
Connected to
Logically positioned at
• To infer what a signal is related to, we define its position in a logical circuit
• The logical circuit can be hydraulic, mechanical, heat transfer or machine oriented
• A logical position in a circuit can have a state
• When applicable, change of states are managed
• Now we can deduce the meaning of the two measurements
Signal
Measurement
Transformed Measurement
Direct Measurement
Estimated Value Set-point Command Reference Parameter
Transformation
Force To Load
Quantity
Mass Force Acceleration
Class: Elevator
Status: Not Connected
Class: top of string
Class: Force To Load
Class: Direct Measurement
Quantity: Force
Value: 488538
Class: Transformed Measurement
Quantity: Mass
Value: 49800
Class: Parameter
Quantity:
Acceleration
Value: 9.81
Mechanical element
Top-drive
Top-drive Body
Active Element
ElevatorTop-drive
shaft
Drill-string
top of drill-string
Class: Top-drive body
Class: Top-drive shaft
Status: Connected
Position in a logical circuit
Signal
Measurement
Transformed Measurement
Direct Measurement
Estimated Value Set-point Command Reference Parameter
Transformation
Force To Load
Quantity
Mass Force Acceleration
Class: Elevator
Status: Not Connected
Class: top of string
Class: Force To Load
Class: Direct Measurement
Quantity: Force
Value: 488538
Class: Transformed Measurement
Quantity: Mass
Value: 49800
Class: Parameter
Quantity:
Acceleration
Value: 9.81
Instance ofValue Real-time value
Class Data class
Le
ge
nd
Function Transformation
Transformed to
Input to
Depend upon
Logical elementState
Connected to
Logically positioned at
Logic op. Validity condition
Conditioned by
Is True
• The validity of a signal may be expressed by a Boolean operation (==, <, >, etc.) between a left and a right argument or a predicate
• The arguments can be any signal, i.e. measurement, parameter, the state of a logical position, etc.
Here, the tension in the top-drive shaft is only valid when the shaft is connected
Signal Types and Dimensions
• Signals can be of different nature:• Measurement: e.g., pump-rate measurement (measured by a sensor)• Set-point: e.g., pump-rate set-point (defined by the driller)• Command: e.g., pump-rate command (controlled by the pump drive)• Estimated value: e.g., maximum flow-rate to avoid fracturing the well• Parameter: e.g., stoke volume of the mud pump
Flow-rate set-point Pump-rate command Stroke volume parameter
Flow-rate measurement
t
Pump-rate Set-Point
Pump-rate command
Max Pump-rate estimated value
Pump-rate measured
Pump-ratePump-rate
Flow-rate
Stroke volume parameter
Signal Types and Dimensions
• Signals can be of different nature:• Signals can have different dimensions:
• Scalar: e.g., pump pressure• Vector: e.g., triaxial acceleration• Interval:
• e.g., acceptable bounds for pump rate while drilling to ensure hole cleaning, avoid formation fracturing and minimize risk of formation washout
• E.g., maximum bounds for pump pressure to detect pipe washout, pack-off, bit nozzle plugged, etc.
• Spatially distributed: e.g. along string annulus pressure measurements• 1D: e.g. estimated tension along the drill-sting• 2D: e.g. resistivity image log• 3D: e.g. estimated temperature in borehole and vicinity
Scalar value: hook-load
Vectorial value: acceleration
Spatially distributed value: ASM Annulus pressure
Spatially 2D value: resisitivity log
Transferring data: Latency, sampling rate and consistency
• In some cases, we want the shortest latency possible, for instance because the signal is used to control a machine or to activate a safety trigger
• In other circumstances, we need signals that are resampled, for instance because the available sampling rate is incompatible with linearity constraints of a mathematical model (CFL condition for finite difference method)
• Furthermore, it may be necessary that several signals are synchronized, for instance because the group of signals define the boundary conditions of a set of partial differential equations.
• A consumer application may need both low latency for certain functionalities, a specific sampling rate with full consistency of a subset of different signals for other functions.
It is not possible to respect simultaneously a low latency and high consistency, therefore there is a need for at least two interfaces: a pure streaming interface that insure low latency, and a resampling interface that provides high consistency
5s samplingt0
2s samplingt0+1s
2s samplingt0+1s
5s samplingt0+2.5s
0,5s samplingt0+10s
0.5s samplingt0+10s
0.5s samplingt0+10s
0.5s samplingt0+10s
DDHub layers
DD-Hub
• OPC-UA
•Standard for data exchange
•Used in many industries for control
Data transfer protocol
•Developed by IRIS nowSemantical data representation
•Several interfaces for different priorities
•Based on 3rd party software libraries
• Internal developments (IRIS)
Data server / aggregation
OPC-UA: • provide a toolbox of tools to exchange information in real-time and build semantical models• However, there is a complete freedom on how to build a semantical model. • All semantical models are valid, but not all models will allow for advanced inferences
Methodology
• Define use cases • Check how signals can be described by the current semantical model
• Possibly extend the semantical model• Verify that a «consumer» application can uniquely interpret the meaning of the signals and make proper decisions
when there are possible choices• Repeat until no more use cases
Projects and activities9 May 2018
• The work on the drilling signal semantical data model is performed in the project: P1.3 «Drilling Process Optimization» that is financed by the DrillWell Centre for Research-based Innovation with the following partners: the Norwegian Research Council, Statoil, Wintershall, AkerBP, ConocoPhillips.• A first prototype (limited to the 4 use cases) has been demonstrated Oct 17• The work continue until Summer-2018 where a first complete semantical data model shall be available
• In parallel, a JIP is started for testing and validating the DDHub concept. This is a so-called Demo2000 project with funding from the Norwegian Research Council, Statoil, AkerBP, TOTAL, ENI, Halliburton• The objective is to develop a first reference implementation that also include two APIs (one for low latency and one for high
consistency)• And then, test whether seamless communication can be achieved when associating multiple drilling data sources and consumer
applications (without human involvement)• The participants to the JIP have a strong influence on which use cases will be used to validate the concept and first-hand to bring
improvements to the semantical data model• If the demonstration is successful, then the semantical data model and its associated API will be passed to a standardization
organism like Energistics (or others).• The standard will be open and vendors can implement it in their software solution. They can compete on performance and
additional features.
Conclusions
• Aggregation of multiple sources
• Importance of semantic in signal description
• Low latency vs High consistency
• Allow for continuous discovery
AcknowledgementsWe would like to thank the Research Council of Norway and DrillWell funding partners, Statoil, Wintershall, AkerBP, ConocoPhillips, and further DEMO2000 and additional partners in the Drilling Data Hub demonstration project, TOTAL, ENI and Halliburton.
I would finally like to acknowledge fellow Chief Scientist Eric Cayeux at IRIS and my colleagues in the IRIS DDHub development team.
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Thank you
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