Fundamentals of Probabilistic Production Forecasting With Reservoir Simulation
This accelerated class covers the basics of greenfield and brownfield probabilistic production forecasting with design of experiments workflows. Throughout the class, a synthetic reservoir model is used to demonstrate workflows. The high-level generic workflows can be applied with a variety of different software packages.
The first part of the class covers development of the center-point reservoir model and identification of reservoir uncertainties. Development of uncertainty ranges for each of the reservoir uncertainties is presented. This section concludes with QATs (Quality Assurance Tests) that are used to validate the center-point reservoir model.
The second part of the class covers greenfield probabilistic production forecasting. The workflow includes selection of experimental designs, selection of response variables, development of proxy equations, Monte Carlo simulation, and selection of deterministic reservoir models (e.g. P10, P50, and P90 reservoir models).
The third part of the class briefly covers history matching of the synthetic reservoir model. Before starting a brownfield probabilistic production forecasting study, some history matching is typically required to ensure that the input uncertainty ranges can provide satisfactory history matches.
The final section of the class covers brownfield probabilistic production forecasting. The greenfield and brownfield workflows are contrasted, and procedures for filtering-out invalid history matches during the Monte Carlo simulation are presented. This section concludes with the selection of low, mid, and high deterministic reservoir models for production forecasting and development plan optimization.
Location:
Online - No recording