Study Identifies the Seven Wastes of Reservoir Modeling Projects
This paper evaluates learnings from the past 30 years of methods that aim to quantify the uncertainty in the subsurface using multiple realizations, describing major challenges and outlining potential ways to overcome them.
The complete paper evaluates learnings from the past 30 years of reservoir modeling projects in the oil and gas industry. Specifically, it considers methods aimed at quantifying uncertainty in the subsurface using multiple realizations, also known as ensemble-based methods. The authors highlight what they perceive to be the major challenges that companies currently face, which they call the “Seven Wastes of Reservoir Modeling Projects,” and outline potential ways to overcome these challenges based on lessons learned from more than 100 modeling projects worldwide during the past 10 years.
The Challenge of Reservoir Modeling
Reservoir modeling dictates that an effort must be made to solve an inverse problem wherein the unknown reservoir model components are assessed given measured static and dynamic data. The following three approaches to achieving this goal often are mentioned in the literature:
- The base-case approach, in which a single best-guess estimate is built or a digital copy of the unknown reservoir parameters
- The multiple stochastic approach, in which multiple models are built by generating realizations from the probability distribution of the unknown reservoir parameters using an automated framework
- The multiple scenario approach, in which multiple (deterministic) models are built in a manual or semiautomated fashion
The strengths and weaknesses of each method are detailed in the complete paper.
Regardless of which approach is used, the process used to help guide the work is perhaps an even-more-important factor for project success or failure.