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Probabilistic knowledge-based characterization of conceptual geological models

The construction of conceptual geological models is an essential task in petroleum exploration, especially during the early stages of investment, when evidence about the subsurface is limited. In this task, geoscientists recreate the most likely geological scenarios that led to potential accumulation of reserves in a target block, based on past experience, historical analogues, and interpreted “signatures” that were left in the data by physical processes. Due to cognitive constraints, this task has traditionally focused on the single most likely conceptual scenario, or at most, a reduced set of scenarios chosen a priori via ad-hoc methods, which often lead to improper block valuation and severe money losses. In this work, we propose a probabilistic framework for reasoning about conceptual geological scenarios that helps domain experts maintain multiple hypotheses throughout the exploration program. The framework is extensible and can be instantiated automatically from simple knowledge templates, a form of “knowledge standard” in the company. We show how the acquired knowledge can be leveraged for uncertainty mitigation using concepts from information theory, and assess the framework qualitatively in a real case study.

Ketersediaan
114551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
13 hlm PDF, 2.837 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.10, June 2021
Subjek
Probabilistic graphical models
Bayesian network
Probabilistic reasoning Information gain
Uncertainty quantification
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Probabilistic knowledge-based characterization of conceptual geological models
    The construction of conceptual geological models is an essential task in petroleum exploration, especially during the early stages of investment, when evidence about the subsurface is limited. In this task, geoscientists recreate the most likely geological scenarios that led to potential accumulation of reserves in a target block, based on past experience, historical analogues, and interpreted “signatures” that were left in the data by physical processes. Due to cognitive constraints, this task has traditionally focused on the single most likely conceptual scenario, or at most, a reduced set of scenarios chosen a priori via ad-hoc methods, which often lead to improper block valuation and severe money losses. In this work, we propose a probabilistic framework for reasoning about conceptual geological scenarios that helps domain experts maintain multiple hypotheses throughout the exploration program. The framework is extensible and can be instantiated automatically from simple knowledge templates, a form of “knowledge standard” in the company. We show how the acquired knowledge can be leveraged for uncertainty mitigation using concepts from information theory, and assess the framework qualitatively in a real case study.
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