Boyd Russell

A Practical Guide to Unconventional Petroleum Evaluation

Petroleum ClubNovember 28, 2012

Reservoir Evaluation Production Optimization – Special Interest Group

Outline

• Issues with Unconventional Plays

• Declines How much

production data is needed?

Which

decline method to use?

Auto-

forecasting—why it’s critical.

• Creating Type Wells Type well mechanics &

sequence bias

Percentile

type wells

Grouping

Issues

• Recap

Issues with Unconventional Plays

Issues with Unconventional Plays

• What are Shale Plays?– Production from tight shale which requires hydraulic

fractures to be productive.

• Shale plays are game changers.

– New technologies.

– Modify existing technologies.

– Move from exploration to “manufacturing”.

Declines

How Much Production Data is Needed?

How much production data is needed?

• Back casting study on 58 ideal Barnett Shale gas wells

• 6 & 7 years of history for all wells

• Individual well forecasts can be unreliable, even with 5 years of data.

• Mean EUR can be very accurate with 2 to 3 years of data.

• The EUR of individual well forecasts is pessimistic in the first years.

0 1 2 3 4 5 6-100%

0%

100%

Time, Years

Perc

en

t V

ari

ati

on

Mean Well

Individual Wells

58 Wells with 6-7 Years History

perfect data

Unconventional Road Show

Declines

Which Decline Method to Use?

Choice of decline equation

• Traditional forecasting methods use Arps equations.

• For shale plays, Arps uses a super hyperbolic period with a smooth transition into an exponential or shallow hyperbolic tail.

• Arps offers no information as to when this transition occurs.

• The transition to exponential may be abrupt. Would require both the limiting decline and time when it commences.

• Choice of Transition Methods – Limiting decline– Elapsed time from start of history– Elapsed time from start of prediction (5 Year Equation)

5 Year Equation – How It Works

60

600

6000

0 5 10 15

Rate

, m

cf/

d

Producing Time, years

Measured

Future Da ta

Predic tion

Forecast 12 Months of Data

Transition

60

600

6000

0 5 10 15

Rate

, m

cf/

d

Producing Time, years

Measured

Future Da ta

Predic ition

Forecast 24 Months of Data

Transition

60

600

6000

0 5 10 15

Rate

, m

cf/

d

Producing Time, years

Measured

Future Da ta

Predic tion

Forecast 3 Years of Data

Transition

Choice of decline equation

• Shale production is characterized by a Transient Flow Period followed by Boundary Dominated Flow (BDF).

• Stretched Exponentials were created to replace the combined use of two Arps equations.

• Unfortunately, they are no better than Arps in predicting the start of BDF.

• These Stretched Exponentials have their problems:– Difficult to solve.

– Difficult to match both early and late time simultaneously.

– Require longer history than Arps.

– Difficult to manipulate.

Comparison of Decline Methods

Unconventional Road Show

Declines

Auto Forecasting – Why It’s Critical

Auto Forecasting – Why It’s Critical• Resource plays are statistical

– Need to forecast 1000s of wells accurately.

• Manual forecasts are not practical– Too time consuming and subjective.

• 10,000 Barnett Shale gas wells were forecast for this study. – Would take an evaluator 5 months to forecast manually

• Manual Forecasting is not easy for unconventional plays– Hard to get a unique super-hyperbolic fit.

Achieving Accurate Auto-Forecasts

• Cannot forecast from start to end of production with one segment

• Expect multiple internal trends and be able to identify them all

• Rate all identified trends based on proximity to today, length of trend and error

• Cannot just use last x number of years for your forecast.

Multiple decline trends embedded in history

Most recent trend is inclining

Unconventional Road Show

Achieving Accurate Auto-Forecasts

• Forecasts need to automatically reject bad data points– Not based on fixed %, but well specific based on data volatility

• Need automatic fail-safes – Forecast inclining

– Recent rates differ from trend

– Every other abnormal event

Multiple bad data points

Creating Type Wells

Type Well Mechanics & Sequence Bias

Type Wells – Why Are They Important?• Traditional petroleum exploration and development relies

on what the author describes as nearology

• With unconventional wells, many factors other than geology can have as much or more of an impact on EUR

• Thus analog or type wells forecasts are used extensively, especially during a well’s early production period

• The main uses for type wells

– Predicting reserves to obtain financing

– Valuating project to make investment decisions

– Determining future production and cash flow

Type Wells – How Are They Built?• The Industry Standard Practice (ISP) of creating type

wells is to average the production rate from contributing wells

• Rarely includes individual well forecasts and relies solely on production

• This ISP is defective

• Using combined historical production with reliable production forecasts remedies the defect

Type Wells – Flawed Methodology?• Forecasts are

implicit to the ISP method

• Implicit forecasts are usually inaccurate

• Type well quality is compromised

• Better forecasts yield better type wells

Unconventional Road Show

Type Wells – Sequence Bias• Wells without production (gap wells) are not filled with

representative rates

• Profit Optimization– Best wells drilled first– Implicit forecasts for the newer wells created from older,

better wells– Type wells are optimistic

• Technical Play– Wells improve as technology develops– Implicit forecasts for the newer wells created from older,

poorer wells– Type wells are pessimistic

Sequence Bias In The Hugoton FieldEUR Error

mmcf %Aug 2011 Forecast 1466

History to 1994History & Forecast 1439 -2%

EUR Errormmcf %

Aug 2011 Forecast 1466

History to 1994History & Forecast 1439 -2%History - 100% 1085 -26%History - 75% 1948 33%History - 50% 2049 40%History - 25% 2562 75%

The Winter Field26 Depleted Cummings Wells Drilled From 1988 to 1993

• Observed trends using only history are exponential – type well inaccurate

• Accurate type well using history and prediction

• Observed trends from history are hyperbolic – type well inaccurate

• Accurate type well using history and prediction

0 50 100 150 200 250

0

50

100

150

200Data to Dec 1994

History 70% cutoff

History 50% cutoff

Cumulative Oil, mbbl

Oil R

ate

, bbl/d

Type Wells

Percentile Type Wells

Unconventional Road Show

Percentile Type Wells• Evaluators need EUR percentile type wells (P10 - P50 -

P90)

• Two methods are used to build percentile type wells

1. Time Slice

• Sort rate for each month• For each month, select the target percentile rate• Determine EUR based on constructed well• Requires strong correlation between rate and EUR (rarely the

case)• Designed for history only, but prediction required (sequence

bias)

2. Selected Wells

• Select wells with EUR similar to the desired percentile ranking• Build a type well using the selected wells

Unconventional Road Show

P75 Type Well For The Hugoton Field

• Selected Well Method– matches EUR from

probability distribution

– Rate truncated to economic limit causes slight drop in EUR

• Time slice Method– No visible trend with

only history

– Predicts only 50% of EUR when created with history and prediction

P75 Type Well For The Hugoton Field

P75 Time Slice uses of wells and full probability range

P75 Type Well For The Hugoton Field

The Time Slice method will not reliably return the desired EUR

• Black line 45o

– The exact answer desired = actual

• Red Dots– EUR distribution for

Hugoton wells

• Blue Dots– Time slice EUR

– Deviates significantly from desired EUR

– Not reliable, even when prediction is used

Type Wells

Grouping Issues

Use of Cross-plots to Create Groups

• Statistically valid groups of wells will exhibit a lognormal distribution.

• There is rarely a strong correlation between IP and EUR.

• Grouping wells or creating type wells based on IP or rate can lead to erroneous answers.

• Time slice type wells are examples of the rate based method.

Dealing With Bias

Barnett Vintage Bias

• In order to maximize profit, the best wells are drilled first (profit bias).

• After 2006, the EUR continually increases (technical bias).

Barnett Operator Bias

• Despite overall improvement in field recovery, Company 1 shows little improvement.

• Company 4 exceeds industry average.

Recap• Resource plays are statistical.

• Resource plays are game changers that require new technologies.

• Decline Curve Analysis requires a minimum two to five years of historical data to be reliable.

• Individual well forecasts are not reliable when based on early production data.

• Stretched exponentials, were developed to replace Arps and address transient flow issues.

• Stretched exponential characteristics:– Complex and difficult to solve.

– Data manipulation is convoluted.

– Do not handle multiple flow regimes.

– Not better at forecasting than using Arps with a five year or other suitable transition.

Recap• Easy and accurate auto-forecasting is required to perform

large resource studies.

• Auto forecasting algorithms must handle multiple flow regimes, bad data, operational issues and any other eventuality and select the most appropriate segment for the forecast.

• Auto fit algorithm must converge and provide the best fit.

• Building analogs requires statistically valid groups and proper type curves.

• Always combine history with an accurate forecast when creating type curves.

• When creating percentile type wells, the selected well approach should be used and the time slice method should be avoided.