Uncertainty Quantification in Reservoir Prediction: Part 2—Handling Uncertainty in the Geological Scenario

Abstract

Models used for reservoir prediction are subject to various types of uncertainty, and interpretational uncertainty is one of the most difficult to quantify due to the subjective nature of creating different scenarios of the geology and due to the difficultly of propagating these scenarios into uncertainty quantification workflows. Non-uniqueness in geological interpretation often leads to different ways to define the model. Uncertainty in the model definition is related to the equations that are used to describe the modelled reality. Therefore, it is quite challenging to quantify uncertainty between different model definitions, because they may include completely different model parameters. This paper is a continuation of work to capture geological uncertainties in history matching and presents a workflow to handle uncertainty in the geological scenario (i.e. the conceptual geological model) to quantify its impact on the reservoir forecasting and uncertainty quantification. The workflow is based on inferring uncertainty from multiple calibrated models, which are solutions of an inverse problem, using adaptive stochastic sampling and Bayesian inference. The inverse problem is solved by sampling a combined space of geological model parameters and a space of reservoir model descriptions, which represents uncertainty across different modelling concepts based on multiple geological interpretations. The workflow includes building a metric space for reservoir model descriptions using multi-dimensional scaling and Geosci (2019) 51:241-264 classifying the metric space with support vector machines. The proposed workflow is applied to a synthetic reservoir model example to history match it to the known truth case reservoir response. The reservoir model was designed using a multi-point statistics algorithm with multiple training images as alternative geological interpretations. A comparison was made between predictions based on multiple reservoir descriptions and those of a single one, revealing improved performance in uncertainty quantification when using multiple training images.

3-D Structural geological models: Concepts, methods, and uncertainties

“…One possibility is the generation of a set of model realizations, based on different scenarios, and to apply suitable distance measures, as already mentioned above for the work of Suzuki et al (2008), but also used widely in the field of multiple point geostatistics (e.g. Demyanov et al, 2015Demyanov et al, , 2018Park et al, 2013;Jung et al, 2013, see also Sec. 4.3.1).…”

Uncertainty Quantification in Reservoir Prediction: Part 1—Model Realism in History Matching Using Geological Prior Definitions

“…A subsequent (part 2) paper (Demyanov et al 2018) demonstrates how to expand this workflow to deal with multiple geological scenarios/interpretations captured with different training images using further machine learning techniques.…”

“…The main application of this technique is to detect novelty, outliers and rare events in a high-dimensional RKHS. A more thorough description of SVM is given in the part 2 paper (Demyanov et al 2018).…”

CO2 storage capacity estimation under geological uncertainty using 3-D geological modeling of unconventional reservoir rocks in Shahejie Formation, block Nv32, China

“…Therefore, geological uncertainties also occur in the petrophysical models. Consequently, the models utilized for reservoir forecasting are vulnerable to various forms of uncertainty; moreover, interpreting and quantifying uncertainty is the most challenging aspect of modeling (Demyanov et al 2019). Therefore, we considered the geostatistical parameters for the geological uncertainties.…”