1. Developments for the Business Planning Add-on: Bidding Analysis
April 25, 2019
John D. Grace
2019 GOM3 User Conference

2. Bidding Models 1.0 Bid/No-Bid model estimates: Bid-Amount model
From the public information we can measure characteristics of blocks with respect to the variables we think bidders associate with profitability of exploration on those blocks
Companies preferences with respect to these characteristics that we measure through analysis of where they did and didn’t bid in the past.
Bid/No-Bid model estimates:
Probability that each open block receives ≥ 1 bid
Probability that each company will bid on each open block
Top 20 players modelled individually
Next 20 players modelled as single “composite” company
Classification of each company revised annually
Bid-Amount model
Forecasts most likely value of high bid for each open block
Produces a single estimate for all bidders (i.e., “high bid” is estimated but not do not try to forecast who submitted the high bid for each block)
Advantages of general approach
Results based on objective, consistent & transparent methodologies
Provides benchmarks & evaluation of outliers
Forecasts with explicit measures of uncertainty
Replicability for validation and sensitivity analysis
2019 GOM3 User Conference

3. Grouping Bidding Companies
Individually Modelled Companies
Company
Money Exposed (Million $2017)
Number of Bids
Money Exposed
(Million $2017)
High-Activity Companies
Medium-Activity Companies (cont)
Shell
$1,419
285
Ecopetrol
$212
121 BP
$777
336
Stone*
$195 87
Equinor
$895
189
Cobalt
$179
108
Chevron
$889
284
Venari
$175 62
ExxonMobil
$914
239
Red Willow
$170 79
BHP
$618
223
Total
$158
120
Anadarko
$443
185
Murphy
$156 64
Medium-Activity Companies
Repsol
$138 74
LLOG
$306
115
Ridgewood
$124 67
Noble
$237 84
Houston Energy
$59 89
Hess
$265 69
Low-Activity Companies Modelled Collectively
Apache
Davis
Fieldwood
Nexen
Tana
Arena
Deep Gulf
Focus
Nippon
W&T
ATP
Energy Res. Tech.
Gulfslope
Petrobas
Walter
Calypso
ENI
MCX
Rising Nat. Res.
Wild Well
CL&F
Enven
Navitas
Talos
Woodside

4. Bid/No-bid Methodology
Logistic Model Equation
𝑌 𝑖,𝑡 = 1 1+exp( − 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑋 1,𝑡 +…+ 𝛽 𝐾,𝑡 𝑋 𝐾,𝑡 ) + 𝜀 𝑡
Global variables:
Oil price
Change in oil price
Local company
variables:
Current co. leases
Past co. leases
Past co. relinquish.
Past co. bids
Co. AOI
Local general
variables:
Water depth
Distance to nearest:
Platform
Discovery
Drilling
Field
Current, past &
relinquished leases
Past bids
Newly available block
Run by Company
Top 20 companies
run individually
Next 20 companies
run as a single
“composite” co.
Co. classifications
changed annually
2019 GOM3 User Conference

5. Bid/No-Bid: Results (Sale 249) Anadarko
Training Period: 2014 – 2017 (Sale 247)
2019 GOM3 User Conference

6. Bid/No-Bid: Results All Companies
Training Period: 2009 – 2013 – Forecasting: 2014
2019 GOM3 User Conference
Year: 2016
Year: 2015
Year: 2014
Year: 2017

7. Bid/No-Bid Accuracy
2019 GOM3 User Conference

8. Bid Amount Methodology
Linear Model Equation
𝑌 𝑖,𝑡 = 𝛽 0,𝑡 + 𝛽 1,𝑡 𝑋 1,𝑡 +…+ 𝛽 𝐾,𝑡 𝑋 𝐾,𝑡 + 𝜀 𝑡
Global, local &
company
variables:
Largely same as in
Bid/No-bid model
Analysis done in
“real” dollars Y
Greater
emphasis on
near-by & past
bids:
Largely same as in
Bid/No-bid model
Estimated
collectively for
all companies
for each block
𝒍𝒐𝒈(𝒀)=𝟖.𝟕𝟓+𝟎.𝟎𝟎𝟕𝑿 X
2019 GOM3 User Conference

9. Bid Amount: Results (Sale 249) Bid Amount: Distribution of Bid Amount
Training Period: 2014 – 2017 (Sale 247)
2019 GOM3 User Conference

10. Problem of Extreme Bids
$630K
Over-estimate
$980K
$70K
Over-estimate
$80K
$16.7 Million
Under-estimate
Hess: $18.3
BOEM (MROV): $3.6
Chevron: $2.8
ESA: $1.8
Shell: $1.6

11. Bidding Models 2.0: New Directions
Competition: Relation of number of bidders & bid amount?
Integration of geoscience into models
Explore relationship between of bid/no-bid & high bids
Changing statistical method: more robust for outliers
Revise rule on removing blocks from analysis
Provide user more detail on output
2019 GOM3 User Conference

12. Bidding 2.0: New Module: Rejections
Between 5 – 7% of high bids are rejected as “too low”
Gov’t raises MROV near established production
Gov’t sets >80% of frontier MROVs to min. legal bid
Two modeling approaches:
Forecasting the MROV on each open block
Forecasting the probability that MROV = MLB
Different statistical model: decision trees/random forests
2019 GOM3 User Conference

13. Proximity to Productivity Raises MROV
Red: PUQ
Light Blue: PUQ - Ring1
Dark Blue: PUQ - Ring2

14. Frontiers Are Highly Bimodal

15. Will MROV = Min. Legal Bid?0% NO
YES
Block rejected last time?
Block unleased
> 3 years?
Block next to
> 1 current lease?
88%
70%
Block next to lease
with MROV > $11 mil.?
90%
63%
2019 GOM3 User Conference

16. Probability MROV = MLB

17. Estimated MROV & Possible Rejections