Abstract: Strategic Exploration in Basin Analysis Applications: Quantitative Measures of Probable Uncertainty and Risk

S. Cao, A. E. Abbott, Ian Lerche

Basin modeling development can be separated into three stages. The first stage is model development during which developing mathematical and computer models is the main theme. As basin modeling techniques are widely used in hydrocarbon exploration, the uncertainty and sensitivity associated with basin modeling become major issues (the second stage). For a given model, we have to examine how the model results are influenced by the change (uncertainty) in the model assumptions, parameters of the model, and of errors in, and finite sampling of, the input data used as control information. The third stage is risk analysis in basin modeling. Because of the uncertainties associated with basin modeling, the model results are not absolutely correct, and should be assigned a risk factor.

In principle, risk analysis in basin modeling should take a Monte Carlo approach (i.e., simulating probability distributions of model results by considering all possible values of assumptions, parameters, data effects, and all possible outcomes of the uncertainties). Unfortunately, the Monte Carlo approach is not appropriate in practice, because the many uncertainties and variables that are associated with basin modeling mean that it takes too much computing time to complete even a few Monte Carlo simulations. In this paper, we present a cumulative probabilistic procedure for risk analysis in basin modeling which is numerically quick, and which provides a risk analysis assessment without loss of accuracy. The procedure does not require massive Monte Carlo computer runs and so is of im ortance in providing risk assessments in a timely manner.

Applications of the cumulative probabilistic method are given to two real case histories to illustrate how one can assess the scientific risk associated with variations and uncertainties, and also to show how one can discriminate sensitive from insensitive controls on the risk factors.

AAPG Search and Discovery Article #90986©1994 AAPG Annual Convention, Denver, Colorado, June 12-15, 1994