Methods for quantifying the uncertainty of production forecasts: a comparative study

Abstract

This paper presents a comparison study in which several partners have applied methods to quantify uncertainty on production forecasts for reservoir models conditioned to both static and dynamic well data. A synthetic case study was set up, based on a real field case. All partners received well porosity/permeability data and ‘historic’ production data. Noise was added to both data types. A geological description was given to guide the parameterization of the reservoir model. Partners were asked to condition their reservoir models to these data and estimate the probability distribution of total field production at the end of the forecast period. The various approaches taken by the partners were categorized. Results showed that for a significant number of approaches the truth case was outside the predicted range. The choice of parameterization and initial reservoir models gave the largest influence on the prediction range, whereas the choice of reservoir simulator introduced a bias in the predicted range.

Prediction under Uncertainty on a Mature Field

“…The test case of this paper is derived from the PUNQS case which was originally used for comparative inversion studies in the European PUNQS project [1].…”

“…To achieve these different objectives while avoiding a prohibitive number of reservoir simulations, several advanced statistical methods are proposed in this paper. Thus, we aim at providing a global methodology to manage the uncertainty on a mature reservoir [1].…”

Integrating Data of Different Types and Different Supports into Reservoir Models

“…In this section, we focus on the PUNQ-S3 field case defined in the "Production forecasting with UNcertainty Quantification" project (Floris et al, 2001). We aim to build 10 constrained models following the methodology described above.…”

Quantifying uncertainty in infill well placement using numerical simulation and experimental design: case study

“…Some other common methods include Monte Carlo technique (Hammersley and Handscomb 1964), derivative tree technique (Steagall and Schiozer 2001), and statistical theory (Venkataraman 2000). Floris et al (2001) had provided comprehensive comparison of the performance, in terms of accuracy, of some existing methods used for quantification of uncertainty of production forecasts. However, detail review on uncertainty estimation and analysis can be obtained from Amaefule and Keelan (1989) , Akinwumi et al (2004), Ballin et al (2001), and Alhuthali et al (2006).…”