Probabilistic Modelling for Decision Support in Drilling Operations · Drilling Basics: Assembling the drill pipe Drill pipe comes in 30ft \joints" Pre-assembled into 90ft \stands"

$Page 1: Probabilistic Modelling for Decision Support in Drilling Operations · Drilling Basics: Assembling the drill pipe Drill pipe comes in 30ft \joints" Pre-assembled into 90ft \stands"$
Probabilistic Modellingfor Decision Supportin Drilling Operations

Martin Giese

University of Oslo

Uni Karlsruhe, 8 May 2009

Martin Giese (UiO) CODIO Modelling 8 May 2009 1 / 23

The CODIO project

COllaborative Decision support for Integrated Operations

NFR Petromacs projectI Total budget: 23.2 MNOKI Funding: 9.0 MNOK (39 %)

Large industrial/research consortiumI Computas (Project leader: Roar Fjellheim)I Conoco PhilippsI Odfjell DrillingI Kongsberg IntellifieldI UiOI UiSI IRISI IFEI IO-Center

Project duration: 1.5.2007 – 30.4.2010


CODIO: Project Objective

To develop a system that enables teams performing well planning andexecution to make operational decisions that optimise overall valuecreation

. . . based on decision science methodology, techniques, and tools usedin a real-time setting (as opposed to off-line decisionse.g. investments)


CODIO: Project Objective

To develop a system that enables teams performing well planning andexecution to make operational decisions that optimise overall valuecreation

. . . based on decision science methodology, techniques, and tools usedin a real-time setting (as opposed to off-line decisionse.g. investments)


Integrated Operations (IO)

Drilling operations controlled from onshore drilling centresI Often leading roles onshore.

Visualisation of processes

Data links to contractors etc.

Increasingly remote work

High use of communicationtechnology


Information Overflow

Perception:

More information, in less time, means better decisions

Reality:

DecisionQuality

Information Load

InformationOverload

Perceived Quality



Perception:


Reality:

DecisionQuality

Information Load

InformationOverload

Perceived Quality



Perception:


Reality:

DecisionQuality

Information Load

InformationOverload

Perceived Quality


Drilling Basics: Mud

Drilling fluid (“mud”)

pumped in through the drillpipe

returned outside the drillpipe

lubricant

coolant

carries up cuttings...

counter-balance for pore pressure

carries up gas entering the well

But:Too high mud weight destroys well


Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant






Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant






Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant






Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant

carries up cuttings

...





Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant






Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant






Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant






Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure





lubricant

coolant






Annulus

Drillpipe

Drill Bit

Mud flow out

Mud flow in

Rock

Porepressure

Drilling Basics: Casings

Drill with increasing mud weight

Run a casing when the pressure getstoo high in some part of the hole.

fill up with cement outside casing

continue drilling with highermud weight

Problems with casing:

Expensive (takes several days)

Smaller diameter ⇒ slower production


Porepressure

Casings

Cement










Porepressure

Casings

Cement










Porepressure

Casings

Cement










Porepressure

Casings

Cement










Porepressure

Casings

Cement










Porepressure

Casings

Cement










Porepressure

Casings

Cement

Drilling Basics: Casing Plan

Casing plan is part of the drilling program

Given the expected

I pore pressureI fracturation pressureI pressure tolerance of casing structure

at any depth. . .

. . . plan how to drill with as few casings as possible

Corresponds to a “staircase” curve in the diagram




Given the expected

I pore pressureI fracturation pressureI pressure tolerance of casing structure

at any depth. . .






Given the expectedI pore pressure

I fracturation pressureI pressure tolerance of casing structure

at any depth. . .






Given the expectedI pore pressureI fracturation pressure

I pressure tolerance of casing structure

at any depth. . .






Given the expectedI pore pressureI fracturation pressureI pressure tolerance of casing structure

at any depth. . .







at any depth. . .







at any depth. . .







at any depth. . .







at any depth. . .







at any depth. . .




Drilling Basics: Assembling the drill pipe

Drill pipe comes in 30ft “joints”

Pre-assembled into 90ft “stands” (16)

Rhythm:

I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:






Rhythm:






Rhythm:I drill for length of one stand

I stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motor

I pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bit

I switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)

I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipe

I detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipe

I screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next stand

I turn pumps and motor back onI lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back on

I lower drill stringI continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill string

I continue drilling





Rhythm:I drill for length of one standI stop motorI pull out drill string a bitI switch off mud pump (4)I “Grip” the drill pipeI detach top drive (18) from drill pipeI screw in the next standI turn pumps and motor back onI lower drill stringI continue drilling


The Scenario (CODIO Case 1)

Drilling close to a formation with high gas pressure

Plan to set a casing at a given depth

When gas comes into the wellbore:

I might just be a gas pocket=⇒ drain or control with mud weight

I might be formation boundary, higher than expected=⇒ might have to set an extra casing=⇒ very costly



















When gas comes into the wellbore:I might just be a gas pocket

=⇒ drain or control with mud weight







=⇒ drain or control with mud weightI might be formation boundary, higher than expected

=⇒ might have to set an extra casing=⇒ very costly






=⇒ drain or control with mud weightI might be formation boundary, higher than expected

=⇒ might have to set an extra casing=⇒ very costly


Miocene formation

Gas detected!Depth:

4685 ft MD

Relevant Real-Time Data

Depth

Gas readings

Mud density, Mud flow, etc.

RPM, rate of penetration, weight on bit (rock strength)


Relevant Real-Time Data

Depth

Gas readings

Mud density, Mud flow, etc.

RPM, rate of penetration, weight on bit (rock strength)


29-Jun-0229-Jun-0229-Jun-02

17:30:00

18:00:0029-Jun-0229-Jun-0218:00:00

17:30:00

18:00:0029-Jun-0229-Jun-02

4744.6

4758.3

4768.3

4740.1

4737.9

4737.6

4737.3

4737.3

4705.8

4733.7

4768.8

4768.8

4744.6

4758.3

4768.3

4770.2

4770.2

4770.2

4770.2

4770.2

4770.2

4770.2

4770.2

4770.2

4682

468546904695469947054711471647214726473247374743474847534759476447674770

5625.0

5380.0

5870.0

5702.5

3905.0

3414.1

3180.3

0.0

2162.0

2285.0

2265.0

2142.5

2117.5

3385.0

3165.0

2420.0

2429.1

0.0

4.2

1545.1

3465.5

3471.7

3497.7

3487.6

3394.5

3415.7

3393.2

3385.8

3348.7

3388.7

3374.3

3371.2

3359.4

3377.3

3377.2

3369.0

3388.3

3380.5

44.1

59.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

886.9

0.0

0.0

MAX GAS 17.6%

0.8

0.9

1.0

1.0

1.3

4.3

7.8

10.9

11.5

15.0

15.0

12.7

7.9

4.9

4.3

4.6

4.7

3.1

1.8

0.8

21.8

21.8

21.8

21.8

21.8

21.8

21.8

21.8

21.8

21.8

21.9

21.9

21.9

21.9

21.9

21.9

21.9

22.0

22.0

21.9

185.5

222.2

222.8

223.1

230.3

223.0

221.5

234.0

222.1

230.9

237.8

238.9

243.9

249.0

247.0

251.8

252.5

252.0

261.1

264.018:00:0029-Jun-024768.8

4768.3

4768.3

4768.1

4767.5

4770.2

4770.2

4770.2

4770.2

4770.2

1545.1

0.0

0.0

0.0

0.0

0.0

0.0

3000.0

59.9

3009.2

3419.0

2782.8

61.8

2998.0

3420.6

3397.6

0.0

879.1

886.9

741.4

0.0

878.9

886.9

886.9

0.8

0.5

1.7

1.5

1.5

0.7

1.8

2.2

21.9

22.0

22.0

22.0

22.0

22.0

22.1

22.1

264.0

264.5

257.6

258.2

262.9

260.4

250.9

250.6

29-Jun-02

2552.5 3355.3 886.9 15.0 22.2227.1

Configuration of Model

Model incorporates various kinds of data:

Geological:I expected depth of formation boundariesI expected pore pressure (gas in formation)I expected rock strength

...

Financial: expected cost/benefit of various operations

All configuration data required with uncertainties: P10/P90, standarddeviation, etc.

=⇒ extremely difficult to elicit





...








...








...





Modelling Technology

Used Bayesian Networks for probabilistic modelling

Modelling follows causal connections:drilling into high-pressure formation⇒ increased gas pressure down-hole⇒ gas pressure higher than mud pressure⇒ gas influx⇒ gas show after lag time

typically plug in data at end of causal chain, compute probabilities forother variables.

well-established technique with commercial tool support


Bayesian Networks

Mathematically: a representation of a joint probability distribution ofseveral random variables.

Compact representation if some of the variables are (conditionally)independent

I often the case in practice


Bayesian Network Example

RainingF 0.7

T 0.3Sprinkler

F 0.8

T 0.2

Wet Grass

Raining F TSprinkler F T F T

F 0.98 0.1 0.05 0.01

T 0.02 0.9 0.95 0.99



RainingF 0.7

T 0.3Sprinkler

F 0.8

T 0.2

Wet Grass


F 0.98 0.1 0.05 0.01

T 0.02 0.9 0.95 0.99

P(Raining ∧ ¬Sprinkler ∧Wet) = 0.3 · 0.8 · 0.95



RainingF 0.7

T 0.3Sprinkler

F 0.8

T 0.2

Wet Grass


F 0.98 0.1 0.05 0.01

T 0.02 0.9 0.95 0.99

P(Raining | ¬Sprinkler ∧Wet)



RainingF 0.7

T 0.3Sprinkler

F 0.8

T 0.2

Wet Grass


F 0.98 0.1 0.05 0.01

T 0.02 0.9 0.95 0.99


=P(Raining ∧ ¬Sprinkler ∧Wet)

P(¬Sprinkler ∧Wet)



RainingF 0.7

T 0.3Sprinkler

F 0.8

T 0.2

Wet Grass


F 0.98 0.1 0.05 0.01

T 0.02 0.9 0.95 0.99


=P(Raining ∧ ¬Sprinkler ∧Wet)

P(Raining ∧ ¬Sprinkler ∧Wet) + P(¬Raining ∧ ¬Sprinkler ∧Wet)


Bayesian Network Tools

Software (commercial and free) exists to

construct Bayesian networks

compute all possible conditional probabilities

learn networks (including structure) from data...


Influence Diagrams (IDs)

A tool for expressing a decision problem.

In addition to Bayesian networks, incorporates:I Option nodes – decision points with possible actionsI Ordering of observations and option nodesI Utility nodes – giving the value of some action in a given situation

Tools can help to find the optimal action after entering observations


Challenges

1. IDs are meant for modelling a fixed series of predetermined decisions.The scenario calls for a continuous decision process (thresholds, timespent waiting, limit utility)

2. IDs require complete information about uncertainties (SDs,confidence, P10/P90. . . ) and utilities, hard to come by.

3. Assigning a utility to influence of extra casing on future production isdifficult.

4. IDs do not provide feedback on “guessed” parameters


Challenges






Challenges






Challenges






Challenges (cont.)

5. Models from engineering experts often depend on parameters forwhich no a-priori estimates are available.

6. Recommendation may depend on data from different points in time(lag time, past gas influx, rock data during connections)

7. Evolution of pressure gradients, etc, is the critical uncertainty, notsingle values


Challenges (cont.)





Challenges (cont.)





Modelling uncertain evolution

Anticipated pressure gradients



Mismatch of anticipated and actual values



Adapting the prediction by locally shifting upwards


Top-level model


Incorporating Future Decisions

Hard decisions need to be made in the end

Need to reflect this in utilities

Had to come up with “tricky” ad-hoc nodes:

I Will be able to contain gas?I Will have to set premature casing?

Possible, but unsystematic, requires deeper domain knowledge

Requires manual analysis of optimal overall strategies, fed back intothe model, instead of having ID technology determine the strategy.





















Had to come up with “tricky” ad-hoc nodes:I Will be able to contain gas?

I Will have to set premature casing?







Had to come up with “tricky” ad-hoc nodes:I Will be able to contain gas?I Will have to set premature casing?


















Lessons Learnt

Use rules of thumb from practitioners, instead of theoretical models

Get full information early (ASCII dumps of logs, etc)

Get simulated logs early

In next round, make it easy to run the model against a log quickly

Be aware of ID limitations, think about them early

Factoring of geodata evolution uncertainty worked fine

Generation of network tables from geodata worked fine


Lessons Learnt









Lessons Learnt









Lessons Learnt









Lessons Learnt









Lessons Learnt









Lessons Learnt









Documents

Probabilistic Modelling for Decision Support in Drilling Operations · Drilling Basics: Assembling the drill pipe Drill pipe comes in 30ft \joints" Pre-assembled into 90ft \stands"