Uncertainty Assessment of Stimulation Design—Bakken Case Study

Abstract Numerical simulation models that are used for well stimulation planning are usually complex and are prone to error. These models have many factors that generally contain uncertainty which, in turn, lead to uncertainty in the model outputs. Uncertainty assessment (UA) can describe such uncertainties in the modeling results. It is always important to the future development plans that uncertainty be properly assessed. This paper presents an approach to the uncertainty assessment of simulation models used for the design optimization of hydraulic fracturing. The use of numerical simulation in petroleum engineering, and the presence of uncertainty in all aspects of design and modeling, may lead to questions such as: What confidence do we have in model results? What are the limits in terms of applicability of model results? Uncertainty assessment together with sensitivity analysis can provide the answers to such questions. The Monte Carlo simulation is employed for implementing such analyses that includes repeated random sampling from input probability distributions. In the assessment of uncertainty conducted for the Bakken case study discussed in this paper, the following seven-step procedure is implemented: describing assessment goals, describing assumptions and constraints, describing parameters and outputs, classifying factor uncertainty, conducting the sensitivity analysis, conducting the uncertainty assessment, and presenting the results with probability curves. The main goal of this study is to present the use of probabilistic modeling for assessing the uncertainties in the simulation of hydraulic fracturing and reservoir performance. Such probabilistic modeling can also help us to develop proxy models for approximating outputs at un-simulated points and to demonstrate the simulation results with probability distribution curves.