Updating Joint Uncertainty in Trend and Depositional Scenario for Exploration and Early Appraisal Stage

摘要

In the early stage development of a reservoir, facies modeling often focuses on the specification and uncertainty regarding the depositional scenario. However, in addition to well data, facies models are also constrained to a spatially-varying trend, often obtained from geophysical data. While uncertainty in the training image has received considerable attention, uncertainty in the trend receives little consideration. In many practical applications, the trend is often as uncertain as the training image, yet is often fixed, leading to unrealistic uncertainty models. We address uncertainty in the trend jointly with uncertainty in the depositional scenario, represented as a training image in multi-point geostatistics. The problem is decomposed into a hierarchical model. Total model uncertainty is divided into first uncertainty in the training image, then of variability modeled in the trend given that training image. The result is that the joint uncertainty in trend and training image can be easily updated when new information becomes available, such as newly available well data. We present the concepts of this approach and apply them to a real-field case study involving wells drilled sequentially where, as more data becomes available, uncertainty in both training image and trend are updated to improve characterization of the facies.