Skip to main content

Pre-drill Assessments and Drilling Outcomes in Mexico in 2018–2022 and Historical Experience from Norway and the Netherlands: Lessons Learned and Recommendations for Future Petroleum Exploration

Abstract

This study compares key pre-drill exploration assessments and drilling outcomes for conventional petroleum prospects/wells evaluated and drilled in Mexico in 2018–2022. All data come from open sources, which facilitates an independent and unbiased performance analysis of the exploration industry (21 operating companies). The study includes 375 exploration prospects/wells and 80 drilled wells with at least some known exploration outcomes. The geological success rate (58%) is much higher than the average pre-drill probability of geological success (PoS, 35%). Explorers, in general and on average, significantly overestimated geological risks before drilling and made many more discoveries than expected. The 71 drilled wells with known volumetric outcomes delivered the total recoverable resources [2966 million barrels of oil equivalent, mmboe (1 million barrels of oil equivalent = 158,987 m3 of oil.)] close to the total average risked expectation (3325 mmboe), which is a good outcome. However, assessments of success case volumes for individual prospects were rather poor. The majority of discoveries contain recoverable resources that differ by a factor of two or more (from 12 times less to 190 times more) from the pre-drill assessed volumes. The national Mexican company Pemex, on average, significantly underestimated the success-case volumes, while the international oil companies, on average, significantly overestimated them. Based on this study of recent petroleum exploration in Mexico and previous similar studies in Norway and in the Netherlands, we conclude that the pre-drill assessments of geological PoS values and success-case resource volumes have not improved over the last 30 years. We recommend that exploration companies should focus on improving forecasting abilities of the explorers.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15

Notes

  1. *1 million barrels of oil equivalent = 158,987 m3 of oil.

References

  • Alexander, J. A., & Lohr, J. R. (1998). Risk analysis: Lessons learned. SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 27–30 September 1998 (SPE Paper 49030)

  • Armstrong, S. J. (2001). Combining forecasts. In S. J. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 417–439). Kluwer Academic Publishers

    Chapter  Google Scholar 

  • Bagley, G., & Bond, M. (2018). Prospect risk and volume benchmarking using Wildcat. Report by Westwood Global Energy Group, p. 28

  • Bar-Hillel, M. (1980). The base-rate fallacy in probability judgements. Acta Psychologica, 44, 211–233

    Article  Google Scholar 

  • Binns, P., & Corbett, P. (2012). Risk and uncertainty from frontier to production–a review. First Break, 30, 57–64

    Article  Google Scholar 

  • Blystad, P., & Søndenå, E. (2005). Exploration history on the Norwegian Continental Shelf, 1990–200: expectations and results. In A. G. Doré, & Vining, B. A. (Eds.), Petroleum geology: North-West Europe and global perspectives (pp. 63–68). Proceedings of the 6th Petroleum Geology Conference

  • Boyd, B. (2019). Anadarko petroleum exploration lookback 2004–18: Lessons learned for conventional exploration risk and uncertainty predictions. Houston Geological Society Bulletin, 62, p. 21. Available at https://www.roseassoc.com/wp-content/uploads/2021/02/Boyd-2019-Anadarko-approved.pdf (accessed on June 3, 2021)

  • Bratvold, R. B., & Begg, S. H. (2010). Making good decisions. Society of Petroleum Engineers

  • Canada–Newfoundland & Labrador Offshore Petroleum Board (2021). Geoscience information. https://www.cnlopb.ca/information/geoscience/ (Accessed 27 October 2021)

  • Capen, E. C. (1976). The difficulty of assessing uncertainty. Journal of Petroleum Technology, 28, 711–715

    Google Scholar 

  • Carstens, H. (2021). Bad news: Reserves tend to get smaller. Geo365. Available at https://geo365.no/olje-og-gass/bad-news-reserves-tend-to-get-smaller/ (accessed on November 21, 2021)

  • Clapp, R. V., & Stibolt, R. D. (1991). Useful measures of exploration performance. Journal of Petroleum Technology, 43(10), 1252–1257

    Article  Google Scholar 

  • Cowgill, B., & Zitzewitz, E. (2015). Corporate prediction markets: Evidence from google, ford, and firm X. The Review of Economic Studies, 82(4), 1309–1341

    Article  Google Scholar 

  • Da, Z., & Huang, X. (2020). Harnessing the wisdom of crowds. Management Science, 66, 1847–1867

    Article  Google Scholar 

  • Delfiner, P. (2003). Modeling dependencies between geologic risks in multiple targets. SPE Reservoir Evaluation and Engineering, 6, 57–64

    Article  Google Scholar 

  • Melbana Energy (2019). Corporate presentation. In RIU Good Oil Conference. Available at: http://www.melbana.com/site/cpfile/2937_1/MAY_2146213.pdf. (Accessed 27 October 2021)

  • Lundin Energy (2021). 2021 Capital Markets Day. Available at: https://www.lundin-energy.com/investors/investor-pack/ (Accessed 27 October 2021)

  • Fosvold, L., Thomsen, M., Brown, M., Kullerud, L., Ofstad, N., & Heggland, K. (2000). Volumes before and after exploration drilling: Results from the project: Evaluation of Norwegian wildcat wells (Article 2). In K. Ofstad, J. E. Kittilsen, & P. Alexander-Marrack (Eds.), Improving the exploration process by learning from the past, Norwegian Petroleum Society Special Publication 9 (pp. 33–46). Norway: Haugesund. https://doi.org/10.1016/S0928-8937(00)80007-7

    Chapter  Google Scholar 

  • Gehman, H. M., Baker, R. A., & White, D. A. (1975). Prospect risk analysis. In J. C. Davis (Ed.), Probability methods in oil exploration (pp. 16–20). Stanford University

    Google Scholar 

  • Goldberg, L. R. (1970). Man versus model of man: A rationale, plus some evidence, for a method of improving on clinical inferences. Psychological Bulletin, 73, 422–432

    Article  Google Scholar 

  • Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: A meta-analysis. Psychological Assessment, 12(1), 19–30

    Article  Google Scholar 

  • Guzmán, A. E. (2013). Petroleum history of Mexico: How it got to where it is today. Search and Discovery Article #10530. Available at https://www.searchanddiscovery.com/pdfz/documents/2013/10530guzman/ndx_guzman.pdf.html

  • Harbaugh, J. W. (1984). Quantitative estimation of petroleum prospect outcome probabilities: An overview of procedures. Marine and Petroleum Geology, 1, 298–312

    Article  Google Scholar 

  • Hoetz, G., Ecclestone, M., & Van der Kraan, V. (2020). Drilling portfolio performance and the role of survival bias in volume estimates. In EAGE 2020 Annual Conference and Exhibition. https://doi.org/10.3997/2214-4609.202011058.

  • Holmes, M., Beeks, W., & Major, T. (1985). A new method of estimating risk-adjusted reserves and economic potential of exploratory prospects. In R. E. Megill (Ed.), Economics and the explorer (pp. 71–84). American Association of Petroleum Geologists

    Google Scholar 

  • Janssen, L. (2019). An offshore exploration drilling review. In EBN exploration day. Available at: https://kennisbank.ebn.nl/wp-content/uploads/2019/12/EBN-Exploration-Day-2019-An-Offshore-Exploration-Drilling-Review-by-EBN.pdf (Accessed 4 March 2021).

  • Johns, D. R., Squire, S. G., & Ryan, M. J. (1998). Measuring exploration performance and improving exploration predictions - with examples from Santos’s exploration program 1993–96. APPEA Journal, 38, 559–569

    Article  Google Scholar 

  • Kahneman, D. (2011). Thinking, Fast and Slow (p. 499). New York: Farrar, Straus and Giroux

    Google Scholar 

  • Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgement (p. 464). New York: Little, Brown Spark

    Google Scholar 

  • Kahneman, D., & Tversky, A. (1973). On the psychology of predictions. Psychological Review, 80, 237–251. https://doi.org/10.1037/h0034747

    Article  Google Scholar 

  • Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human decisions and machine predictions. The Quarterly Journal of Economics, 133, 237–293

    Google Scholar 

  • Koehler, J. J. (1996). The base rate fallacy reconsidered: Descriptive, normative, and metodological challenges. Behavioral and Brain Sciences, 19, 1–53

    Article  Google Scholar 

  • LaCosta, W. C. P., & Milkov, A. V. (2022). Petroleum exploration portfolios generated with different optimization approaches: Lessons for decision-makers. Journal of Petroleum Science & Engineering, 214, 110459

    Article  Google Scholar 

  • Lewis, C. J. (1984). Are our oil and gas resource assessments realistic? AAPG Bulletin, 69, 500

    Google Scholar 

  • Far Limited (2018). In The Gambia, FAR’s Next Frontier. In RIU Good Oil Conference. Available at: https://www.far.com.au/wp-ontent/uploads/2018/09/20180914-InvestorPresentationRIUGoodOilConference.pdf. (Accessed 23 October 2021)

  • Lyon, D., & Slovic, P. (1976). Dominance of accuracy information and neglect of base rates in probability estimation. Acta Psychologica, 40, 287–298

    Article  Google Scholar 

  • van Mastrigt, P., & Quinn, M. J. (2021). Reducing uncertainties to shape the future of exploration. In International Petroleum Technology Conference. IPTC-21339-MS. https://doi.org/10.2523/IPTC-21339-MS.

  • Meehl, P. E. (1954). Clinical vs. statistical prediction: A theoretical analysis and a review of the evidence (p. 149). University of Minnesota Press

    Google Scholar 

  • Meisner, J., & Demirmen, F. (1981). The creaming method: A Bayesian procedure to forecast future oil and gas discoveries in mature exploration provinces. Journal of the Royal Statistical Society, Series A (General), 144(1), 1–31

    Article  Google Scholar 

  • Milkov, A. V. (2015). Risk tables for less biased and more consistent estimation of probability of geological success (PoS) for segments with conventional oil and gas prospective resources. Earth Science Reviews, 150, 453–476

    Article  Google Scholar 

  • Milkov, A. V. (2017). Integrate instead of ignoring: Base rate neglect as a common fallacy of petroleum explorers. AAPG Bulletin, 101(12), 1905–1916

    Article  Google Scholar 

  • Milkov, A. V. (2020). Forecasting abilities of individual petroleum explorers: Preliminary findings from crowdsourced prospect assessments. Journal of Petroleum Geology, 43(4), 383–400

    Article  Google Scholar 

  • Milkov, A. V. (2021). Reporting the expected exploration outcome: When, why and how the probability of geological success and success-case volumes for the well differ from those for the prospect. Journal of Petroleum Science & Engineering, 204, 108754

    Article  Google Scholar 

  • Möller, U. (2015). Prospect appraisal: Learning from past performance. Available at: https://www.roseassoc.com/wp-content/uploads/2021/02/Moeller-2015-Wintershall-approved.pdf (Accessed on June 3, 2021)

  • Murphy, A. H. (1993). What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather Forecasting, 8, 281–293

    Article  Google Scholar 

  • Murtha, J. A. (1994). Incorporating historical data into Monte Carlo simulation. SPE Computer Applications, 6(2), 11–17

    Article  Google Scholar 

  • Nederlof, M. H. (1994). Comparing probabilistic predictions with outcomes in petroleum exploration prospect appraisal. Nonrenewable Resources, 3(3), 183–189

    Article  Google Scholar 

  • Norwegian Petroleum Directorate (2018). Resource report 2018 - exploration. Available at: https://www.npd.no/en/facts/publications/reports2/resource-report/resource-report-2018/ (Accessed October 27, 2021)

  • Ofstad, K., Kullerud, L., & Helliksen, D. (2000). Evaluation of Norwegian wildcat wells (Article 1). In K. Ofstad, J. E. Kittilsen, & P. Alexander-Marrack (Eds.), Improving the exploration process by learning from the past, Norwegian Petroleum Society Special Publication 9 (pp. 23–31). Norway: Haugesund

    Google Scholar 

  • Ofstad, K., Øvretveit, A., Kullerud, L., & Heggland, K. (2000). Probability of discovery and the reasons for dry wells: results from the project: Evaluation of Norwegian Wildcat Wells (Article 3). In K. Ofstad, J. E. Kittilsen, & P. Alexander-Marrack (Eds.), Improving the exploration process by learning from the past, Norwegian Petroleum Society Special Publication 9 (pp. 47–55). Norway: Haugesund

    Google Scholar 

  • Otis, R. M., & Schneidermann, N. (1997). A process for evaluating exploration prospects. AAPG Bulletin, 81, 1087–1109

    Google Scholar 

  • Pennycook, V., & Thompson, V. A. (2016). Base-rate neglect. In R. F. Pohl (Ed.), Cognitive illusions: Intriguing phenomena in judgement, thinking and memory (pp. 44–61). Oxon

    Google Scholar 

  • Quirk, D. G., Archer, S. G., Keith, G., Herrington, P., Ramirez, A. O., & Bjørheim, M. (2018). Can oil and gas exploration deliver on prediction? First Break, 36(10), 83–88

    Article  Google Scholar 

  • Reed, A., Ericson, S., Bazilian, M., Logan, J., Doran, K., & Nelder, C. (2019). Interrogating uncertainty in energy forecasts: The case of the shale gas boom. Energy Transit, 3, 1–11

    Article  Google Scholar 

  • Rose, P. R. (2001). Risk analysis and management of petroleum exploration ventures. AAPG Methods in Exploration Series 12

  • Rose, P. R. (1987). Dealing with risk and uncertainty in exploration: How can we improve? AAPG Bulletin, 71, 1–16

    Google Scholar 

  • Rudolph, K. W., & Goulding, F. J. (2017). Benchmarking exploration predictions and performance using 20+ yr of drilling results: One company’s experience. AAPG Bulletin, 101(2), 161–176. https://doi.org/10.1306/06281616060

    Article  Google Scholar 

  • Ryan, V. R. (1979). The development of the Mexican petroleum industry to 1914. Master of Arts Thesis, Rice University, p. 125

  • Schneider, M., & Cook Jr., D. M. (2017). Drilling a downdip location: Effect on updip and downdip resource estimates and commercial chance. Search and Discovery Article #42102. Available at: http://www.searchanddiscovery.com/pdfz/documents/2017/42102schneider/ndx_schneider.pdf.html (Accessed 5 March 2021)

  • Simperl, E. (2015). How to use crowdsourcing effectively: Guidelines and examples. Liber Quarterlu, 25, 18–39

    Article  Google Scholar 

  • Sluijk, D., & Parker, J. R. (1986). Comparison of predrilling predictions with postdrilling outcomes, using Shell’s prospect appraisal system. In D. D. Rice (Ed.), Oil and gas assessment - Methods and applications, AAPG Studies in Geology 21. pp. 55–58

  • Snow, J. H., Dore, A. G., & Dorn-Lopez, D. W. (1996). Risk analysis and full-cycle probabilistic modelling of prospects: a prototype system developed for the Norwegian shelf. In A. G. Dore & R. Sinding-Larsen (Eds.), Quantification and Prediction of Petroleum Resources, Norwegian Petroleum Society (NPF) Special Publication 6 (pp. 153–165). Amsterdam: Elsevier

    Google Scholar 

  • Sorrell, S., & Speirs, J. (2010). Hubbert’s legacy: A review of curve-fitting methods to estimate ultimately recoverable resources. Natural Resources Research, 19(3), 209–230

    Article  Google Scholar 

  • Stabell, C. B. (2000). Alternative approaches to modeling risks in prospects with dependent layers. In SPE Annual Technical Conference and Exhibition (SPE Paper 63204). https://doi.org/10.2118/63204-MS.

  • Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations (p. 297). Doubleday

    Google Scholar 

  • Sykes, M. A., Hood, K. C., Salzman, S. N., Vanderwater, C. J. (2011). Say what we mean and mean what we say: The unified risk model as a force for shared understanding. Search and Discovery Article #70110. Available at: http://www.searchanddiscovery.com/documents/2011/70110sykes/ndx_sykes.pdf (Accessed 5 March 2021).

  • Tennyson, M. E. (2005). Growth history of oil reserves in major California oil fields during the twentieth century. In T. S. Dyman, J. W. Schmoker, & M. Verma (Eds.) Geologic, Engineering, and Assessment Studies of Reserve Growth. U.S. Geological Survey Bulletin 2172-H, 15 p. https://pubs.er.usgs.gov/publication/b2172H.

  • Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction (p. 355). Broadway Books

    Google Scholar 

  • Uman, M. F., James, W. R., & Tomlinson, H. R. (1979). Oil and gas in offshore tracts: Estimates before and after drilling. Science, 205, 489–491

    Article  Google Scholar 

  • United Kingdom Oil & Gas Authority (2021). Exploration data. Available at: https://www.ogauthority.co.uk/exploration-production/exploration/exploration-data/ (Accessed 27 October, 2021)

  • Wachtmeister, H., Henke, P., & Höök, M. (2018). Oil projections in retrospect: Revisions, accuracy and current uncertainty. Applied Energy, 220, 138–153

    Article  Google Scholar 

  • White, D. A., & Gehman, H. M. (1979). Methods of estimating oil and gas resources. AAPG Bulletin, 63, 2183–2192

    Google Scholar 

Download references

Acknowledgements

The author thanks three reviewers for providing comments and suggestions that significantly improved this paper. In addition, the help and insights of Helge Kreutz are greatly appreciated.

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexei V. Milkov.

Ethics declarations

Conflict of Interest

The author has no competing interests to declare that are relevant to the content of this article.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Milkov, A.V. Pre-drill Assessments and Drilling Outcomes in Mexico in 2018–2022 and Historical Experience from Norway and the Netherlands: Lessons Learned and Recommendations for Future Petroleum Exploration. Nat Resour Res (2022). https://doi.org/10.1007/s11053-022-10074-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11053-022-10074-3

Keywords

  • Petroleum
  • Exploration
  • Prospect
  • Well
  • Probability of success
  • Resources