Using Stochastic Charge Modeling Techniques to Understand Oil and Gas Column Uncertainties
Oil and gas column heights in undrilled prospects are routinely computed in 3D petroleum systems modelling (PSM) software to constrain exploration risks. Deterministic approaches are used to estimate the most likely (P50), the high (P10) and the low (P90) values for the hydrocarbon columns. In recent years, stochastic approaches have been introduced also in the 3D PSM modelling approach, and versatile Monte Carlo techniques are typically used. In the approach presented, hydrocarbon generation, expulsion (primary migration), secondary migration, trap fill and spill, and trap leakage processes are simulated through geologic times within a 3D geological model framework.
We present a PSM workflow whose aim it is to characterize model parameters (or groups of them) according to their impact on the estimated column height uncertainties. This workflow includes 4 steps: a stochastic calibration of the entire 3D PSM model using all input parameters (1), leading to a full uncertainty analysis of the hydrocarbon columns (2). This is followed by renewed Monte Carlo modelling run employing randomization of selected, separate parameter groups - e.g. source rock and expulsion; hydrocarbon retention during migration; capillary leakage (3). Only parameters within one group are changed within each of these Monte Carlo simulations. Subsequently, a parameter ranking computation of all parameters - both, globally and within each group - is then used to determine which are the more important parameters influencing the uncertainties (4).
The workflow is demonstrated using a 3D PSM dataset from the North Sea, and important implications for future exploration is discussed. An important contribution from the workflow outlined here is the determination of how much each (group of) parameter(s) contributes to the predicted oil and gas column uncertainties. To determine whether the uncertainties of the source rock or trap leakage parameters contribute the most to the estimated oil and gas column uncertainties may in many cases be the main result of this approach.
The knowledge obtained using the outline workflow adds to the confidence in the predictions and can also be used to target further risk reduction exploration activities.
AAPG Datapages/Search and Discovery Article #90350 © 2019 AAPG Annual Convention and Exhibition, San Antonio, Texas, May 19-22, 2019