Funding and Support for the SPE Distinguished Lecturer Program
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Each year, companies use averaged well production (type wells) to support billion dollar expenditures to buy and develop oil and gas resources. These type wells often have unrepresentative rate-time profiles and recoveries over-stated by as much as 50%. These intolerable errors result from common, but incorrect, assumptions in constructing type well production profiles, and the selection and weighting of analog wells. Literature related to constructing type wells is sparse and incomplete. This lecture will fill that gap and lead participants to informed decisions for best practices in type well construction. Hind casting examples show that only small errors in recovery result when the type well construction combines historical and predicted production rates. This improvement results from using educated estimates (not intrinsic values) for months with no data to average, and from individual well forecast errors that offset one another. A Monte Carlo method incorporates risk and leads to better well selection and weighting factors, achieving more representative rate-time profiles. The recommended methodology incorporates aggregation and choosing different uncertain parameters. Parameter choice is important because it makes little sense to risk recovery (e.g., P90 for proved reserves) when the application demands a different parameter such as present value. Type well construction methods are common, but they have errors that are difficult to detect. Evaluators are likely using type wells for financial analysis, facility design, cash flow prediction, reserve estimation and debt financing without knowledge of the inaccuracies and options to improve accuracy.
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Funding and Support for the SPE Distinguished Lecturer Program
- 1. Primary funding is provided by The SPE Foundation through member donations and a contribution from Offshore Europe The Society is grateful to those companies that allow their professionals to serve as lecturers Additional support provided by AIME Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl
- 2. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl 2 Randy Freeborn Two Vital Secrets for Building Reliable Type Wells
- 3. AGENDA TYPE WELL What is a type well The challenge 1st SECRET All type wells 3 Inherent Errors Case Study 2nd SECRET Probability type wells Time slice method Aggregation method Comparison WRAP UP 3
- 4. TYPE WELL What is a type well? Rate-time production profile Shift representative wells to a common start date Average them to represent new wells Common method comprised of two parts • History average rate until too few wells • Prediction projection of best fit of history 4
- 5. TYPE WELL The Challenge Dr. Lee, 2015 Reserve Summit • SEC’s experience (circa 2008) type wells exceed results by about 25%. 2013 Proprietary Research Report • Drilling results did not meet the objectives set out in 40 of 100 published play specific type wells. • Only 14 of 40 companies consistently met targets. Personal Experience • EUR more likely to be over estimated, as much as 40%. 5Pervasive … Capital Intensive … Errors
- 6. 1st SECRET Applies to All Type Well Methods FORECAST EACH WELL THEN AVERAGE HISTORY & PREDICTION
- 7. 3 ERRORS 1. Forecast groups • Never forecast groups, always group forecasts 2. Survivor bias • Concentrating on things that survive • The treatment of depleted wells • Depleted wells produce with rate = 0 3. No production (declining well count) • Recent wells that have no rate to average • Also a form of survivor bias • Use best available forecast 7 SPE 162630 & 167215
- 8. 3 ERRORS #1 Forecast Groups 8 Group Forecasts New trends are visible Forecast errors cancel Accuracy improves Type well is accurate Forecast Groups Usually no clear trend High quality best fit Bad business decision Grouping masked a trend
- 9. 3 ERRORS #2 Survivor bias 9 Common method Depleted rate = type well rate Creates false rate and reserve Correct treatment Each well must have a rate SPE 162630
- 10. 3 ERRORS #2 Survivor bias 10 Common method Compounding effect SPE 162630
- 11. 3 ERRORS #3 No production 11 Common method Well rate = average rate Best wells drilled first Correct treatment Include every well Use best available forecast AVOID ALL 3 ERRORS Forecast, then average history & prediction
- 12. 3 ERRORS Numerical example 12 Monthly Production Rate Well Month 23 Month 24 Month 25 Month 26 1 1200 1100 1000 900 2 1000 900 800 700 3 35 25 no prod no prod Field Total 2235 2025 1800 1600 Type Well 2235 / 3 = 745 2025 / 3 = 675 1800 / 2 = 900 1600 / 2 = 800 Drill 3 Wells 3 x 745 = 2235 3 x 675 = 2025 3 x 900 = 2700 3 x 800 = 2400
- 13. CASE STUDY 88 Hugoton Kansas wells 13 Data truncated 5 years drilling + 5 years producing Cut off Stop when too few wells Type well Looks reliable SPE 162630
- 14. CASE STUDY 88 Hugoton Kansas wells 14 SPE 162630 1st SECRET average history & prediction History Only Cut Off History & Known To Dec 1996 100% 75% 50% Prediction Nov 2014 EUR, bcf 1.53 1.58 1.74 1.34 1.36 Error, % 13% 16% 28% -2% History Only Cut Off History & Known To Dec 1996 100% 75% 50% Prediction Nov 2014 EUR, bcf 1.53 1.58 1.74 1.34 1.36 Error, % 13% 16% 28% -2%
- 15. Certainty (P10, P50, P90) What is uncertain? (EUR, Present Value, Cash Flow, …) How many wells? 2nd SECRET Applies to Probability Based Type Wells STOP USING THE TIME SLICE METHOD USE THE AGGREGATION METHOD
- 16. TIME SLICE METHOD 16 SPE 62630 & 167215 Uses only history Normally P10, P50 or P90 For Each Month • Sort by rate • Get the P90 or P50/P10 rate • Decline to complete
- 17. TIME SLICE METHOD 17 SPE 62630 & 167215 Probability • What is uncertain? Unknown • No Aggregation (1 well) • Rates from the full distribution • Ignores EUR distribution
- 18. TIME SLICE METHOD SPE 167215 • 9 well example There is a P10 & P90 well • Crossing rate/time Creates additional error
- 19. TIME SLICE METHOD SPE 167215 • Shaded area Rate < P90 or Rate > P10 • P90 low, P10 high Where is the EUR right?
- 20. TIME SLICE METHOD 20 Probability of what? • Cannot choose at value , e.g. EUR, NPV • Type well does not match the EUR Prone to error • Errors from using only history • Crossed rate-time profiles • Rates selected from all wells and probabilities • Doesn’t represent a defined group of wells P90 rates from 19 of 25 wells, P4 to P96 Disadvantages
- 21. AGGREGATION METHOD 21 Resolves 4 type well questions • Which wells to use? • Should wells have equal weighting? • How does one account for drill program size? • What is the right way to handle probabilities? The Approach • Find appropriate weighting factors SPE 175967
- 22. AGGREGATION 101 22 Aggregated Distribution • Pick 5 random probabilities • Get values for each • Average the values
- 23. AGGREGATION 101 23 Aggregated Distribution • Pick 5 random probabilities • Get values for each • Average the values • Repeat 100,000 times • Plot distribution of means Aggregated Results • P90 & P50 values increase • Certainty improves P10/P90 • P90 economic with 5 wells
- 24. AGGREGATION METHOD 24 Step 1 Get Target EUR (237) Step 2 Weighting Factor • Continue 5 well trials • When mean ~ target Tally the selected wells • Tally more than 1000 trials • Calculate weighting factor as a % of the total tally Step 3 Build type well • Multiply history and prediction by the weighting factor and sum
- 25. AGGREGATION METHOD 25 Step 1 Get Target EUR (237) Step 2 Weighting Factor • Continue 5 well trials • When mean ~ target Tally the selected wells • Tally more than 1000 trials • Calculate weighting factor as a % of the total tally Step 3 Build type well • Multiply history and prediction by the weighting factor and sum Calculate Weighting Factors Well EUR Tally 8 175 24 197 1 7 203 25 214 1 9 220 21 241 5 277 16 293 1 17 326 1 3 378 30 396 1 6 434 5 Calculate Weighting Factors Well EUR Tally 8 175 0 24 197 2 7 203 0 25 214 2 9 220 1 21 241 0 5 277 1 16 293 2 17 326 2 3 378 0 30 396 1 6 434 0 10 Calculate Weighting Factors Well EUR Tally 8 175 0 24 197 2 7 203 0 25 214 2 9 220 5 21 241 0 5 277 1 16 293 2 17 326 2 3 378 0 30 396 1 6 434 0 15 Calculate Weighting Factors Well EUR Tally Weight 8 175 81 8.9% 24 197 69 7.5% 7 203 73 8.1% 25 214 28 3.1% 9 220 67 7.3% 21 241 33 3.7% 5 277 53 5.8% 16 293 25 2.8% 17 326 42 4.6% 3 378 4 0.5% 30 396 7 0.8% 6 434 3 0.3% 910 100%
- 26. AGGREGATION METHOD 26 Designed for new drilling • Based on probability of drilling each well Properly uses aggregated probabilities Will use any uncertain parameter Proper ratios for secondary products • Calculated with the correct weighting Aggregation • Increases P90 & P50 reserves • Adds certainty Advantages
- 27. COMPARISON P90 type wells 27 Method is critical I choose the aggregation method Time Slice Comparison (1 well) Btax Atax EUR NPV 10% & EUR $mm $mm mbbl P90 aggregation -0.8 -0.9 191 P90 time slice -3.4 -2.5 111 Difference 2.6 1.7 79
- 28. COMPARISON P90 type wells 28 Benefit of Aggregation Btax Atax EUR NPV 10% & EUR $mm $mm mbbl P90 drill 5 wells 1.1 0.4 237 P90 drilll 1 well -0.8 -0.9 191 Difference 1.9 1.2 46
- 29. TWO VITAL SECRETS As a Type Well Builder Average both history and prediction Use Aggregation method for new drilling As a Consumer of Type Wells Avoid type wells that use only historical data Type wells should represent the number and quality of wells you plan to drill 29
- 30. Society of Petroleum Engineers Distinguished Lecturer Program www.spe.org/dl 30 Your Feedback is Important Enter your section in the DL Evaluation Contest by completing the evaluation form for this presentation Visit SPE.org/dl
Editor's Notes
- My name is Randy Today I plan to share 2 secrets that I have learned from 5 years of researching type wells. When you to return to work, I hope you will only use type wells built with history and prediction.
- I will begin by introducing you to type wells, then the talk will be split into two equal parts, each part dealing with one of the secrets.
- Rate-time profile Shift representative wells to a common start date Average them to represent new wells that we plan to drill Part 1 – history Part 2 - prediction
- The challenge of obtaining accurate type wells comes from 3 factors that are progressive 1. First, type wells are pervasive, used in all aspects and disciplines of our business 2. Second, the decisions we make based on type wells are capital intensive and therefore important 3. Finally, the most popular methods used to create type wells have inherent errors, than may cause us to make bad business decisions Dr. Lee is best known for his role in modernizing the SEC regulations At a recent conference he advised that the SEC is experiencing type wells that exceed results by about 25%. In a 2013 research report 40 drilling programs failed to resultDrillin40 of 100 companies My personal experience is that type wells are commonly created using only historical data. Hind casting studies show those type wells are more likely to overstate EUR by an amount as high as 40%.
- The 1st secret applies to all type wells and is related to 3 errors that are inherent to constructing time wells using only historical data. You can avoid all of the errors by forecasting each well, then building the type well by averaging history and prediction.
- There are three errors inherent creating type wells using only historical data With only history, we average first and then forecast from the average – I call this forecasting groups We should always forecast first and then average – In my words group forecasts Survivor Bias - the logical error of concentrating on the wells that "survived“ We fail to concentrate on depleted wells by dropping them from the calculation post depletion We shift wells to a common start date, sometimes termed normalizing Newer wells will not have produced long enough to have data for averaging We also fail to concentrate on these wells. You will see the nature of these 3 errors, and learn they will all be avoided when you forecast 1st and then average history and forecast to construct type wells
- I will use a hind casting example to demonstrate the concept of grouping forecasts 9 wells producing for 27 continuous years – there is no survivor bias in the data We truncate the data to 8 years to build a type well Use the remaining 19 years to confirm results This is the Common Method The production data has been averaged and then best fit to complete the type well Often hard to fit because of multiple or unclear trends In this case this data is good, leading to a high quality best fit In a moment you will see that the business decision would have been bad When we Group Forecasts We see trends in the data that are often masked in a group Inaccuracy in the forecasts cancel, resulting in higher quality Average history and prediction Observe the resulting type well is near perfect More work to forecast, but reliable auto-forecasting tools are available Large difference between grouping forecasts and forecasting groups is not uncommon
- We have 3 producing wells After 3 years one well is depleted, so we average only 2 wells There is an unintended consequence when the well count is reduced the type well rate behaves as though the depleted well continues to produce at the average rate Every well must have a rate Those that do not have a rate are assumed to continue producing at the average rate The correct treatment is to assume the depleted well continues to produce at a rate of 0
- We have 3 producing wells After 3 years one well is depleted, so we average only 2 wells There is an unintended consequence when the well count is reduced the type well rate behaves as though the depleted well continues to produce at the average rate Every well must have a rate Those that do not have a rate are assumed to continue producing at the average rate The correct treatment is to assume the depleted well continues to produce at a rate of 0
- We have 3 producing wells, then one well runs out of production It is still producing, but hasn’t produced long enough to have a rate for averaging As with the depleted well, the adverse consequence is that missing production gets the average rate That average rate may be OK, but remember we usually hi-grade by drilling our best wells first The new “worst” well gets the average rate from the best wells Replace the intrinsic forecast with your own Forecast wells, the average both history and forecast
- We have 3 producing wells After 3 years one well is depleted, so we average only 2 wells There is an unintended consequence when the well count is reduced the type well rate behaves as though the depleted well continues to produce at the average rate Every well must have a rate Those that do not have a rate are assumed to continue producing at the average rate The correct treatment is to assume the depleted well continues to produce at a rate of 0
- Hind casting evaluation of shallow gas wells 5 years of drilling, plus another 5 years of production 5 to ten years of production for average or forecast Common method 75% cut off – trade off, more data, potential error best wells first – wells with no production cause higher rate. All panels look like reliable type wells.
- Common method yellow line from the previous slide, error band EUR accuracy about 15%, but rate-time profile terrible Forecast the wells, then average the history and prediction Visually compare the type well in white to the measured data in blue We obtain an accurate rate-time profile with an EUR accurate within 2% after 32 years
- You must specify 3 things The Probability of what – EUR, NPV, Other? Level of certainty P90 uncertain parameter with meet or exceed the specified value 90% of the time How many wells will be evaluated with this type well
- Time Slice method Today’s most popular method, used in most computer software. Used to find P10/P50/P90 without regard for the number of wells being drilled.
- Time Slice method Today’s most popular method, used in most computer software. Used to find P10/P50/P90 without regard for the number of wells being drilled.
- This slide intended to show you why the Time Slice method is wrong EUR distribution 9 wells: there is a P90 and P100 well Rate Time chart Yellow lines are the P10 and P90 rate-time profiles Orange lines are the rate-time profiles use min or max production When sorting the P10 = max rate and P90 = min The blue area shows where the P10 type well rate is replaced with the maximum or where the P90 type well rate is replaced with the minimum rate This is a one way exchange, the P10 type well rate and EUR will always get bigger and the P90 type well rate and EUR will always get smaller. Illustrated in blue on the left chart The P10 to P90 ratio will be too high, inferring greater risk than supported by the EUR distribution
- This slide intended to show you why the Time Slice method is wrong EUR distribution 9 wells: there is a P90 and P100 well Rate Time chart Yellow lines are the P10 and P90 rate-time profiles Orange lines are the rate-time profiles use min or max production When sorting the P10 = max rate and P90 = min The blue area shows where the P10 type well rate is replaced with the maximum or where the P90 type well rate is replaced with the minimum rate This is a one way exchange, the P10 type well rate and EUR will always get bigger and the P90 type well rate and EUR will always get smaller. Illustrated in blue on the left chart The P10 to P90 ratio will be too high, inferring greater risk than supported by the EUR distribution
- Inset is 88 well Hugoton using 23 years of data. Read Bullets
- Step 2 & 3 Follow directions on the slide
- Step 2 & 3 Follow directions on the slide
- Step 2 & 3 Follow directions on the slide
- Type well calculated using aggregation method is shown and Compared to the one using the Selected Well method. Selected well is my second choice for building type wells. The type well is build from a subset of the analogs that have an uncertainty parameter (NPV) similar to the target (2.2 million) The answers are statistical, I do not know which is better. I would choose Aggregation because the wells are weighted in proportion to the probability of their being drilled in a 35 well program. Advantages Read list
- Read the list