Multiple-Point Statistics Simulation Models: Pretty Pictures or Decision-Making Tools?

Abstract

Abundant literature has been produced for the last two decades about multiple-point statistics simulation, or MPS. The idea behind MPS is very simple: reproduce patterns from a 2D, or most often a 3D, training image that displays the type of geological heterogeneity deemed to be relevant to the reservoir or field under study, while honoring local data. Replicating an image is a traditional computer science problem. Thus, it should come as no surprise if a growing number of publications on MPS borrow ideas and techniques directly from computer vision and machine learning to improve the reproduction of training patterns. However, quoting Andre Journel, “Geostatistics is not about generating pretty pictures.” Models have a purpose. For example, in oil and gas applications, reservoir models are used to estimate hydrocarbon volumes and book reserves, run flow simulations to forecast hydrocarbon production and ultimate recovery, and make decisions about field development or optimal well drilling locations. Specific key features such as the extent and connectivity of shale barriers may have a major impact on the reservoir performance forecasts and the field development decisions to be made. Those key features that need to be captured in the model, along with the available subsurface data and constraints of the project, should be the primary drivers in selecting the most appropriate modeling techniques and options to obtain reliable results and make sound decisions. In this paper, the practitioners’ point of view is used to evaluate alternative MPS implementations and highlight remaining gaps.