RESERVOIR MANAGEMENT: WATER DRIVE OPTIMIZATIONonline.stat.tamu.edu/dist/analytics/sasday2015/presentations/holdaway.pdfDISTANCE TO WATER LEVEL IN A WELL INFLUENCES WATER CUTS Water

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RESERVOIR MANAGEMENT:

WATER DRIVE OPTIMIZATION


OPTIMIZE PRODUCTION WITH INTELLIGENT WELL MANAGEMENT

2. Explore

8. Development Strategy

9. Risk management

1. Sample 3. Uncertainty

5. Matching

6. Strategy Optimization

7. Probabilistic Strategy

4. Probabilistic Analysis


• Traditional DCA

• Probabilistic methodology

• Well Forecasting Solution • Bootstrapping module

• Clustering module

• Data mining workflow

FUNCTIONAL

DATA ANALYSIS


Paper 167428•Data Mining Methodologies enhance Probabilistic Well Forecasting•K.R.Holdaway

1. Cumulative liquid production

2. Cumulative oil or gas production

3. Water cut (Percentage determined

by water production/liquid

production)

4. B exponent (Decline type curve)

5. Initial rate of decline

6. Initial rate of production

7. Average liquid production

CLUSTER

ANALYSIS


BIG DATA BIG ANALYTICS APPROACH

• Water cut curves normalization based on Cumulative

Liquid produced.

• Analysis should take into account increments wise

regions.

(there are 3 increments)

• Analysis should be based on the different time phases

(time windows)

• Water distance (minimum FWL Distance) should be

considered as one of the inputs in the clustering

• Well bore type (horizontal or vertical) should also be

taken into account

Modeling Design Parameters: It has been decided that the analysis

should be designed according to the following criteria:

Normalization

Spatial

Distribution

Time Windows

Water Distance

Well Bore Type



NORMALIZATION OF THE WATER CUT CURVES

In order to make valid comparisons between well data sets it is necessary to perform a remediation

step that entails a robust quality control work process to identify outliers and impute for missing and

erroneous values.

Well 158_1

Well 1436_0

Normalizing WCT curves for

each well using Cumulative

Liquid produced in this well

Normalization

A Water Cut normalization step is implemented based on Cumulative Liquid production.

That approach helps eliminating temporal aspect of the data to some extent

We refer to the

normalized

scatter plots of

the Water Cuts

as the Water

Cut Marks of

the wells



TAKING INCREMENTS OF HRDH INTO ACCOUNT

Spatial

Distribution

Increment 1

Increment 2

Increment 3

Spatial location of the well will play an important role in the clustering analysis. We

assessed the location with respect to increments

The field is divided

into three

increments,

however increments

are correlated with

production time

windows.

We analyzed the

distributions of the

wells into the

increments in

different time

windows, and

consolidated into

one categorization

where the clustering

analysis should be

based on.



AVOID BIAS IN COMPARING DIFFERENT WELLS

• It has been suggested to use the above time windows for the clustering analysis.

• A separate segmentation model will be developed for each of the three time

windows.

• As note in the spatial distribution section, the well locations are depending on the

increments, and increments are depending on time. Therefore it turns out that by

considering only the time windows one will also incorporate the effect of the

increment information

Production Starting Time

Time window 1 Time window 2 Time window 3

∞ 1 Jan 96 1 Jan 96 1 Oct 2003 ∞

Time Windows

Production has started in different decades in different wells. There are wells that initiated oil

production since 1960s.Therefore production amounts are in different scales for the old and new

wells, and behaviors are different in the depletion, injection and post-injection phases

1 Oct 2003



DISTANCE TO WATER LEVEL IN A WELL

INFLUENCES WATER CUTS

Water Distance

The minimum distance to free water level is an important factor to understand water cut

behavior of a well. Considering the wells in the region the majority of the wells are distributed

within 4000-6600 interval (Mean: 5533 Median: 5388).

An interesting research

question would be to analyze

the different behaviors of the

wells having significant different

FWL Dist values

Distribution of the Minimum Distance to Free Water Level



HORIZONTAL AND VERTICAL WELLS BEHAVE DIFFERENTLY

Well Bore Type

Time Window Number of Horizontal

Wells

Number of Vertical Wells

Window1 (… - 1996)

- 42

Window2 (1996 – Oct.2003)

88 55

Window3 (Oct2003 - …)

138 9

Deviated Wells

1

5

2

Majority of the wells are horizontal and are analyzed in Time Window 3.

There are also a small number of wells having deviated configuration

Distribution of wellbore type for the wells

considered throughout the analysis


WELL CONFIGURATION (HORIZONTAL / VERTICAL)

Distribution of Well Configuration (Horizontal / Vertical) in the Clusters

As it can be seen the distribution is characteristic for some clusters.

Clusters are represented by their numbers

*Deviated Wells Excluded

All wells in this

cluster are vertical


RESULTS / GENERAL COMMENTS

PRESSURE TRENDS (TIME WINDOW 1)

In the early years of the

production both clusters are

following the overall trend

of the wells in the time

window.

After 2003 the annual SWP

average for the wells in

cluster 2 starts increasing

and surpasses that of

cluster 1 although the SWP

trend of both clusters is

positive (probably due to

injection)

Annual Average SWP for the two clusters of Time Window 1

0

500

1000

1500

2000

2500

3000

3500

19

57

19

60

19

63

19

66

19

69

19

72

19

75

19

78

19

81

19

84

19

87

19

90

19

93

19

96

19

99

20

02

20

05

20

08

20

11

SWP

Year

1

2




In the early years of the

production the clusters

are following the overall

trend of the wells in the

time window.

After 2003 and Injection

period the pressure is

measured differently for

the wells in cluster 4. That

cluster has the largest

pressure. It is almost

3500 on the average.


0

500

1000

1500

2000

2500

3000

3500

4000

1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011

SWP

Year

1

2

3

4

Different Average

SWP trends based

on most recent

observations




Since the wells in this time

window are relatively new

and the production started

in 2003, the pressure is

following a steady trend

However, it can be noted

that first and second

clusters are in the declining

phase, whereas 3,4 and 5

are increasing.


0

500

1000

1500

2000

2500

3000

3500

2003 2004 2005 2006 2007 2008 2009 2010 2011

SWP

Year

1

2

3

4

5






Q&A

ENERGY ANALYTICS SUMMIT 2014

RESERVOIR MANAGEMENT: WATER DRIVE OPTIMIZATIONonline.stat.tamu.edu/dist/analytics/sasday2015/presentations/holdaway.pdfDISTANCE TO WATER LEVEL IN A WELL INFLUENCES WATER CUTS Water

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