RESERVOIR SIMULATION

Horizontal Well Placement Guidance Acquisition

by Using GIS and Data Mining Methods

Presenter: Baijie Wang

Supervisors: Prof. Zhangxing (John) Chen and Prof. Xin Wang

Department of Geomatics Engineering

Date:June 16th, 2012

Outline

• Horizontal Well Placement Study – Horizontal Well Placement Attributes

– Association Rule Mining

• Reservoir Simulation – Verify the Association Rule Results

• PetroData-GIS System Prototype

• Conclusion and Future Work

2

HORIZONTAL WELL PLACEMENT

GUIDANCE ACQUISITION

Part 1:

3

Introduction

• Horizontal Well Placement is critical to EOR, such as SAGD – Poor horizontal well placement negatively influence the

efficiency of oil production

• Previous horizontal well placement planning is mainly based on the engineers’ experience

• Large volume of field data has been accumulated

4

Horizontal Well Placement Guidance Acquisition

• Horizontal Well Placement Guidance Acquisition (HWPGA)

– Sort through related SAGD field data and identify interesting

associations between horizontal well placement attributes and oil

production performance.

Horizontal Well

Placement Attributes

Oil Production

Performance Associations

Association Rule

Mining

5

• Example

– Under what well placement condition, the oil production performance is

good?

Horizontal Well Placement Attributes

Toe

(4) Prod_Max_OWC

(2) Prod_Min_OWC

ICP

Horizontal Well:

Producer

Geological Surface:

OWC(3) Prod_Avg_OWC

The average distance between producer and OWC.

(5) Prod_Toe_OWC

(1) Prod_ICP_OWC

Attributes are named with the following rule: [First Name]_[Middle Name]_[Last Name]

Prod

Inj

Max

Avg

Min

ICP

Toe

RT

BITWT8

So50

OWC

× × = 40 Attributes

Total 40 Attributes / Well Pair

Four Geological

Surfaces:

RT

BITW8

So50

OWC

Two Wells:

Injector

Producer

6

Association Rule Mining

• Association rule mining (ARM) is to analyze and present the implicit

strong rules discovered in the well placement dataset. Each of the

discovered rules is in the form:

IF A THEN B Support=n% Confidence=m%

A is the antecedent. B is the consequence.

Support=n% denotes this rule is applicable to n% records in the dataset.

Confidence=m% denotes the accuracy of this rule that under condition A,

there is m% B is going to happen.

Example: If Bread Than Milk, Sup = 10% Conf=87%

• Interestingness means: high support and high confidence

7

Data Collection

• 43 well pairs

• 4 Geological Surfaces

• 40 Horizontal Well

Placement Attributes

• Oil Production

Performance: cSOR

• Algorithm: SE-Apriori

• Support > 15%

• Confidence> 70%

• Resulted in 613 Rules

8

Drilled in 2005

Drilled in 2010

Drilled in 2006

Drilled in 2007

43 Study Well Pairs

A

B

C

Well placement data is modified in this presentation due to confidentiality

Experimental Results

1

( )t

Rule

SensitivityIndex

N Support Rule

Sensitivity Analysis:

N: Number of records

Geological Surfaces

Ranking:

OWC: 924

So50: 638

BITW8: 492

RT: 388

9

Experimental Results

Sample Rules Support Confidence

{Prod_Avg_OWC=2/8} AND {Prod_Max_OWC=2/8} AND

{Prod_Min_OWC=2/8} AND {Prod_Toe_OWC=2/8 } => {SOR: Fair}

14% 71%

{Prod_Avg_OWC=1/8} AND {Prod_Max_OWC=1/8} AND

{Prod_Min_OWC=1/8} AND {Prod_Toe_OWC=1/8 } =>{SOR: Poor}

12% 83%

10

• Example of high dimensional association rules:

Indicate that placing the wells close to bottom water negatively influence the

thermal efficiency of the study SAGD project.

RESERVOIR SIMULATION

Part 2

11

Introduction

• Validate the association rule mining results.

• Study the reservoir response to different horizontal well

placement plans.

12

Hypothetical

Raise by 6 m

Water Saturation

Example:

• Association Rule Mining

Results:

Placing the HZ wells close to

OWC (80% Sw) jeopardize

the oil production efficiency.

• Target:

Raise the HZ wells to 10 m

above OWC

13

Simulation Experiment

Simulation

Model

Hypothetical

Model

History Matched

Model

Field History

Match

History Match

Result

Predicted Result

Raise Wells

by 6 m

Predication

Run

History

Match Run

History Match Model Hypothetical Model

Same Environment: STARTS 2011

Heterogeneous Reservoir Model

Flex Wellbore

Injector Constraint: Injection Rate

Producer Constraint: Total Fluid Production

…

Difference Well Elevation:

4m above OWC

Well Elevation:

10m above OWC

14

Simulation Results

Cum Oil Increases by 8,500 m3

with two years’ production

PETRODATA-GIS PROTOTYPE

Part 3:

15

PetroData-GIS Architecture

PetroData-GIS GUI GIS Functions

Data Mining Functions

Association Rule Mining

Traditional

GIS Functions

Map Visualization

Spatial Query

Navigation

Others

Spatial Database

Nonspatial Data:Well UWI

Production

Well Log

Others

Spatial Data:Well Location

Well Trajectory

Others

• Developed a PetroData-GIS system prototype to help:

– Manage the large volume of field data

– Visualize the association rule mining results

16

17

Main GUI

Map Display AreaEagle Eye View

Menu Bar Table of Layers Contents Tool Bar

Map Scale Coordinates

SE-Apriori

Association Rule Visualization

18

IF Inj_Avg_OWC=1/8 AND Prod_Avg_OWC=1/8 THEN SOR=Poor, Sup=22%, Conf=90%

Conclusion and Future Work

• Conclusion

–Defined a group of horizontal well placement attributes.

– Introduced Association Rule Mining to analyze the influence from

horizontal well placement to the oil production performance.

–Demonstrated the association rule results with simulation.

–Developed a GIS to help the data management and visualization.

• Future Work

– Improve the well placement study by considering well communication.

–Evaluate the potential of other data mining methods in petroleum

applications.

–Continue the development of the PetroData-GIS prototype.

19

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226-231. 20

Acknowledgement

• Thank Dr. John Chen and Dr. Xin Wang for their

guidance and support.

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Sponsors

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Thank You!