1887

Abstract

Summary

During risk assessment, exploration geoscientists routinely evaluate various individual risk segments to estimate the chance of success of a given prospect. Usually, this estimation is based on subjectivity, unconscious biases that may lead to inconsistent evaluations. In this research project, the goal is to constrain these biases using cognitive advisors, supported by machine learning algorithms, to overcome the frequent subjectivity and have a fair prospect ranking. This new methodology is based on a new metric – the Level of Knowledge (LoK) – that relies on the available data and geological model to characterize a given prospect. This parameter is anchored to a powerful source of information – the Knowledge Base – a database that structures peer-reviewed inputs and other unstructured information, such as research papers or reports, giving more accurate advices to geoscientists. Overall results show a positive response by testers, which identify the value of having a normalized metric on available knowledge and experience (LoK), as the base for having consistent and fair assessments on their Probability of Success evaluations. As the system is continuously trained, oil and gas companies that integrate this system would benefit of having a consistent portfolio management, thus, supporting exploration strategies.

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/content/papers/10.3997/2214-4609.201900988
2019-06-03
2020-07-11
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References

  1. CCOP (Coordinating Committee for Offshore Prospecting in Asia)
    , [2000] The CCOP guidelines for risk assessment of petroleum prospects.
    [Google Scholar]
  2. Costa, L., ZalánP. and Nobre, L.
    , [2013] Estimativa da chance de Sucesso Exploratório: uma abordagem em três passos consistente com a classificação de recursos petrolíferos. Petrobras Geosciences. Bulletin, 21 (2), 313–324
    [Google Scholar]
  3. Milkov, A.
    , [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 Reviews150, 453–476
    [Google Scholar]
  4. Otis, R.M. and Schneidermann, N.
    , [1997] A process for evaluating exploration prospects. AAPG Bulletin, 81, 1087–1109
    [Google Scholar]
  5. Polson, D. and Curtis, A.
    , [2010] Dynamic of uncertainty in geological interpretation. Journal of the Geological Society, 167, 5–10
    [Google Scholar]
  6. Rose, P.
    , [2001] Risk Analysis and management of Series, Petroleum Exploration Ventures. AAPG Methods in Exploration, 12
    [Google Scholar]
  7. Surovtsev, D, Levy, T and Sethi, M.U.
    [2019] Look at situations from all angles and you will become more open: Advanced analytics approach to exploration portfolio allocation decisions. AAPG Search and Discovery Article, 70378, DOI:10.1306/70378Surovtsev2019
    https://doi.org/10.1306/70378Surovtsev2019 [Google Scholar]
  8. White, D.
    , [1993] Geologic Risking Guide for Prospects and Plays. AAPG Bulletin, 77 (12), 2048–2061
    [Google Scholar]
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