Open AccessData analytics in asset valuation under uncertainty: A case study of unexplored oilfields
Author(s) -
Fransiscus Rian Pratikto,
Sapto Wahyu Indratno,
Kadarsah Suryadi,
Djoko Santoso
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1072/1/012064
Subject(s) - data mining , computer science , bayesian network , cluster analysis , analytics , a priori and a posteriori , bayesian probability , prior probability , bayesian inference , machine learning , artificial intelligence , philosophy , epistemology
Valuing unexplored oilfields is challenging since it deals with multiple sources of uncertainty and covers a lengthy period. Previous research suggests that the major source of uncertainty in the valuation of unexplored oilfields is reservoir condition. It is usually represented by a bunch of parameters based on which reserve volume and production rates can be estimated. The values of these parameters are revealed only after exploration well is drilled; we refer them as post-discovery parameters. Prior to exploration drilling, data on some reservoir characteristics, called the pre-discovery parameters, are available. This research aims to develop a model to estimate the probability distribution of post-discovery parameters based on pre-discovery data. The model is data-driven, built using the database of proven reservoirs. We use the Bayesian network to develop the model and apply the k-fold cross validation to test the results. By adding a window parameter to the target variable, the model provides information regarding the trade-off between accuracy and confidence. In general, compared to the initial model that uses a priori clustering based on the reservoir’s lithology and depth, our Bayesian network model produces lower variances.