ANS Oil Production ForecastingCWN FISCAL POLICY GROUP DISCUSSIONJANUARY 18, 2019BRIAN HOVE

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ANS Historical Production FY1977-2018 (Mbbl/day)

FY20 Revenue Sources

Unrestricted General FundPetroleum 1,689 16%Non Petroleum 576 5%“POMV” PF Draw 2,933 28%

Designated General Fund 916 9%Other State Funds 722 7%Federal Funds 3,783 36%Total State Revenue $10,619 100%

(Source: Senator Von Imhof’s 1/11/19 presentation to CWN & 2018 Fall RSB)

FY20 Revenue Sources (sans Fed)

Unrestricted General FundPetroleum 1,689 25%Non Petroleum 576 8%“POMV” PF Draw 2,933 43%

Designated General Fund 916 13%Other State Funds 722 11%Total State Revenue $6,836 100%

(Source: Senator Von Imhof’s 1/11/19 presentation to CWN & 2018 Fall RSB)

FY20 Revenue Sources (sans Fed)

Unrestricted General FundPetroleum 1,689 25%Non Petroleum 576 8%“POMV” PF Draw 2,933 43%

Designated General Fund 916 13%Other State Funds 722 11%Total State Revenue $6,836 100%

(Source: Senator Von Imhof’s 1/11/19 presentation to CWN & 2018 Fall RSB)

FY20 Revenue Sources (sans Fed)

Unrestricted General FundPetroleum 1,689 25%Non Petroleum 576 8%“POMV” PF Draw 2,933 43%

Designated General Fund 916 13%Other State Funds 722 11%Total State Revenue $6,836 100%

(Source: Senator Von Imhof’s 1/11/19 presentation to CWN & 2018 Fall RSB)

Critical Revenue Considerations

Projected Value of the Permanent Fund

Projected Permanent Fund Earnings-------

Projected Price of ANS Oil

Projected ANS Oil Production

Good Times

So, forecasting methodology is important. . .

Bad Times

Because we don’t want to get caught out. . .

Background

Review of ANS production forecasting dates back to 2008

Others have also explored this issue, inc. Roger Marks

Current study covers the period FY1989 through FY2018

Source Materials

SOA DOR 12/19/17 memo detailing historical ANS oil production data

SOA DOR Fall Revenue Sources Book (RSB), 1989-2018

Objective

Ascertain reliability of the SOA’s ANS production forecasting methodology by comparing an averaged range of published forecasts against actual production

Process

1. Production forecasts published in RSBs during the period 1989 to 2017 range from a low of eight outlooks to a high of 24, each outlook advances in one year intervals

2. The study is limited to the first ten outlooks in each RSB, data beyond ten years is not considered meaningful

Process

3. Each forecast was compared to actual production reported for the corresponding year

4. Results were recorded as a percentage of actual production and formatted as an array

5. In a given range of RSB publications, all one-year outlooks were summed to derive an average, same with all two-year forecasts, and so on up to ten-years

Process

6. The resulting graph depicts DNR’s forecasting proficiency over three distinct periods:

1989-1992, Group Green 1993-2011, Group Purple 2012-2017, Group Blue

99% 99%

101% 100%

99% 98%95%

91%

102%

107%

113%

118%

124%

129%

132% 132%131%

128%

99% 97% 99%

95%

90%

85%

80%

90%

100%

110%

120%

130%

140%FP1 FP2 FP3 FP4 FP5 FP6 FP7 FP8 FP9 FP10

RSB Average Forecasted Production for Each of Ten Forecast PeriodsFP = Forecast Period

1989 to 2017 RSB ANS Production Forecast Tested Against Actual

1989-1992 1993-2011 2012-2017 Actual

Brian E. Hove/January 2019

Findings

Group Green (1989-92 RSBs) demonstrates good reliability out to the 8th forecasted year

Group Blue (2012-2017 RSBs) demonstrates decent reliability through year three

Findings

Group Purple (1993-2011 RSBs) demonstrates poor reliability even by the 2nd forecasted year

Note the steep linear trajectory peaking at 132% of actual production at the 7th Forecast Period

This means that forecasts in 19 RSBs averaged 132% of actual production for the 7th Forecast Period

Caveats

Group Green (1989-92) is made up of four discrete forecasts for each of the ten periods

This is a fairly small sample upon which to draw firm conclusions about reliability of the underlying methodology used during this period

Group Blue (2012-17) may suffer some of the same issues as Group Green

Average Accuracy Across Each Forecast Period for Selected Periods

1989 - 2017 RSBs 1989 - 1992 RSBs 1993 - 2011 RSBs 2012 - 2017 RSBs

Average Average Average AverageForecasts Forecasts Forecasts Forecasts

Forecast as a % of # of Forecast as a % of # of Forecast as a % of # of Forecast as a % of # ofPeriods Actual Forecasts Periods Actual Forecasts Periods Actual Forecasts Periods Actual Forecasts

FP1 101% 29 FP1 100% 4 FP1 102% 19 FP1 99% 6FP2 104% 28 FP2 99% 4 FP2 107% 19 FP2 97% 5FP3 109% 27 FP3 99% 4 FP3 113% 19 FP3 99% 4FP4 113% 26 FP4 99% 4 FP4 118% 19 FP4 95% 3FP5 117% 25 FP5 101% 4 FP5 124% 19 FP5 90% 2FP6 122% 24 FP6 100% 4 FP6 129% 19 FP6 85% 1FP7 126% 23 FP7 99% 4 FP7 132% 19 FP7FP8 126% 22 FP8 98% 4 FP8 132% 18 FP8FP9 123% 20 FP9 95% 4 FP9 131% 16 FP9FP10 120% 18 FP10 91% 4 FP10 128% 14 FP10FP11 111% 13 FP11 83% 4 FP11 124% 9 FP11 - -FP12 102% 11 FP12 75% 4 FP12 117% 7 FP12 - -FP13 92% 10 FP13 69% 4 FP13 108% 6 FP13 - -FP14 90% 10 FP14 64% 4 FP14 107% 6 FP14 - -FP15 88% 10 FP15 62% 4 FP15 105% 6 FP15 - -FP16 86% 10 FP16 60% 4 FP16 103% 6 FP16 - -FP17 84% 9 FP17 58% 4 FP17 104% 5 FP17 - -FP18 81% 8 FP18 55% 4 FP18 108% 4 FP18 - -FP19 81% 7 FP19 53% 3 FP19 102% 4 FP19 - -FP20 77% 7 FP20 51% 3 FP20 96% 4 FP20 - -FP21 62% 4 FP21 32% 2 FP21 92% 2 FP21 - -FP22 67% 2 FP22 44% 1 FP22 89% 1 FP22 - -FP23 41% 1 FP23 41% 1 FP23 - - FP23 - -FP24 40% 1 FP24 40% 1 FP24 - - FP24 - -

345 83 241 21

Takeaways

Production forecasting is a critical component of the SOA’s financial planning process

The DNR’s new production forecasting methodology appears to be on a more conservative track

Increased confidence will require continued testing of forecasted production against actual production

We may be better served to hone a five year outlook

Thank You