A Connectionist Model for Dynamic Economic Risk Analysis of Hydrocarbons Production Systems | Academic Article individual record
abstract

This study presents a connectionist model for dynamic economic risk evaluation of reservoir production systems. The proposed dynamic economic risk modeling strategy combines evidence-based outcomes from a Bayesian network (BN) model with the dynamic risks-based results produced from an adaptive loss function model for reservoir production losses/dynamic economic risks assessments. The methodology employs a multilayer-perceptron (MLP) model, a loss function model; it integrates an early warning index system (EWIS) of oilfield block with a BN model for process modeling. The model evaluates the evidence-based economic consequences of the production losses and analyzes the statistical disparities of production predictions using an EWIS-assisted BN model and the loss function model at the same time. The proposed methodology introduces an innovative approach that effectively minimizes the potential for dynamic economic risks. The model predicts real-time daily production/dynamic economic losses. The connectionist model yields an encouraging overall predictive performance with average errors of 1.954% and 1.957% for the two case studies: cases 1 and 2, respectively. The model can determine transitional/threshold production values for adequate reservoir management toward minimal losses. The results show minimum average daily dynamic economic losses of $267,463 and $146,770 for cases 1 and 2, respectively. It is a multipurpose tool that can be recommended for the field operators in petroleum reservoir production management related decision making.

authors
publication outlet

Risk Analysis

author list (cited authors)
Mamudu, A., Khan, F., Zendehboudi, S., & Adedigba, S.
publication date
2021
publisher
Wiley Publisher
keywords
  • Dynamic Economic Risk
  • Reservoir Production
  • Loss Function Model
  • Connectionist Model
  • Economic Loss
citation count

0

identifier
586576SE
Digital Object Identifier (DOI)