S

Decline Curve Analysis

at the Push of a ButtonPresented by: Brent Haas 6/9/2016

IntroductionS Who am I?

S Brent Haas – VP of Engineering for R. Lacy Services

S What are we talking about?

S Automated decline curve analysis is a relatively new practice in oil and gas. The need for automated forecasting has arisen from analysis of unconventional resource plays where large well sets need to be analyzed to fully understand the productivity of the play.

S What are the benefits of automated forecasting?

S Speed!

S Relatively unbiased results

S Allows engineer to perform more rigorous analysis of results and iterate the analysis to improve accuracy of estimates

S What are some of the limitations?

S Garbage in, garbage out

S “Black box” results; low confidence in resulting EURs

S Soooo what are we talking about again?

S Today we will discuss some of the techniques that R. Lacy is implementing to improve the accuracy of automated decline curve analysis

Decline Curve Analysis (“DCA”)

S Data Filtering

S Removing operational noise from DCA regression fits

S Dataset Refining

S Identifying well behaved decline curves (higher confidence in DCA results)

S Empirical Decline Equation Selection

S Modified Arps

S Power-Law Exponential

S Stretched Exponential

S Duong

S Modified Duong

S Setting Parameter Constraints

S Example: Setting limits for hyperbolic exponent

S Use of Correlations

S Improve EUR estimates for wells with limited/erratic production history

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Items to Consider for Automated Workflows

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Data Filtering

Data Filtering• Removing operational noise

from well production

histories is an important step

prior to attempting a

regression fit in decline curve

analysis.

• The tool that we use

(developed by Peter Shaw

with TIBCO’s Industry

Analytics Group) employs a

smoothing spline method to

automate this process prior to

running regression analysis

on large well sets.

S

Dataset Refining

Data RefiningIdentifying Benchmark Wells

“Benchmark Well” “Poor Fit Well”

Criteria Used to Identify “Benchmark” Wells

• Minimum cut-offs set for:

• Percentage of total months used in the regression analysis

• Average error of the decline curve versus the actual data (Total history)

• Average error of the decline curve versus the actual data (Last 12 Months)

S

Empirical Decline

Equation Selection

Decline Curve Equations

9

Oil

Rate

(bbl/

d)

Time (years)

Traditional Method

New Methods

Back-testing Analysis

10

Oil

Rate

(bp

d)

Back-testing analysis

• Empirical relations studied

• Arps (Modified Hyperbolic)

• Stretched Exponential

Decline

• Power-law Exponential

• Duong

• 56 wells with eight (or more) years

of historical production data without

significant operational noise.

Single Well Example

56 wells X 10 fits per well X 4 equations = 2,240 forecasts

Forecast Comparison

11

EUR DATA

EUR Vintage (yr)

EU

R (

Mb

bl)

Forecast Comparison

12

EUR Ratio (Convergence)

EUR Vintage (yr)

EU

R R

ati

o (

EU

R/

EU

R8yr )

S

Setting Parameter

Constraints

Hyperbolic Exponent and DiSensitivity in Regression Analysis (>6 years of history)

Uncertainty Region of Hyperbolic Parameters (b, Di)

The elliptical shape represents

the 95% confidence boundary

The cross-plot to the right shows the results of an investigation of the

95% confidence region for the regression fit shown in the rate-time plot

above. Essentially, any combination of b and Di within the elliptical

region results in an equally accurate fit of the production data.

Regression methods converge on a unique solution, but this

uncertainty analysis demonstrates how sensitive the fit of the empirical

equation is to the noise seen in raw production data.

Hyperbolic Exponent and Di

S Increased uncertainty region with less production history

Sensitivity in Regression Analysis (3 years of history)

With only 3 years of history used in the regression analysis, the

uncertainty region has increased. As seen in the cross-plot to the

right, this dataset can be fit equally as well with b-factors ranging

from 0.4 to 1.4 (using different combinations of Di).

Uncertainty Region of Hyperbolic Parameters (b, Di)

Parameter Refinement

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Setting limits for hyperbolic exponent

Hyp

erb

oli

c E

xp

on

ent

Bin by:

Production

Life (yrs)

Peak Rate

Initial Decline

Use box plots to compare distributions of hyperbolic exponents

S

Use of Correlations

Use of Correlations

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Wells with limited, or erratic, production history

18 Month Correlation

Correlations for Months 1-56

Cumulative Production

EU

R

Additional Things to ConsiderS Know your dataset

S Many times automated forecasting is used on public data. Make sure you

understand any limitations such as (these vary by State):

S Lease level reporting

S Allocated production data

S Production history for one well split between multiple Production ID codes

S Implementation of Spotfire dashboards requires engineers to have a

better understanding of MIS basics. This will help engineers

understand the capabilities of Spotfire and communicate to developers

the enhancements that they would like to incorporate into dashboards.

S Know when you draw the line on dashboard development and start

the analysis!

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S

Thank you!QUESTIONS?