The importance of uncertainty quantification and risks assessment in the petroleum industry cannot be overstated. Uncertainty will always be present in production forecasts and reserves estimates. Underestimation of uncertainty when estimating reserves and profitability of projects can lead to poor decision making and disappointment.

Water Displacement Curve (WDC) models are alternative to Decline Curve Analysis (DCS) models in waterflooded oil fields. These curves allow engineers to estimate reserves and forecast production performance in waterflooded oil reservoirs taking into account either liquid or water production. Compared with DCA, WDC models are expected to perform better in forecasting oil production in waterflooded oil fields.

In this study I applied WDC models probabilistically using Bayesian methodology and Markov Chain Monte Carlo (MCMC) stochastic simulation. I also developed a Multimodel approach based on eleven WDC models to quantify uncertainty in production forecasts by assessing differences in matches and forecasts provided by each model.

Both Multimodel and MCMC with WDC models were calibrated and compared to MCMC with DCA method which was recently developed and introduced by Gong et al. (2011). Reliability of the developed methods was assessed using production history of 100 wells from actual waterflooded oil fields. I performed hindcast studies in which I assumed that some fraction of the actual historical production data is known (6, 12, 24 and 36 months) and the rest of the actual production is unknown (5 - 7 years). I then matched the assumed known production fraction of the history and forecasted production to the end of the actual historical period. The cumulative production at the end of the hindcast is compared to the actual cumulative production at this time to test the probabilistic reliability of the methodology when production history is limited. The study showed that both developed methods are well-calibrated probabilistic methods. Also, computer software was developed during this research to make the process of calculations more convenient.

Keywords:
production control, production monitoring, machine learning, Bayesian Inference, Upstream Oil & Gas, mcmc, production forecasting, reserves evaluation, soviet socialist republic, production rate
Subjects:
Well & Reservoir Surveillance and Monitoring, Reservoir Simulation, Formation Evaluation & Management, Reserves Evaluation, Risk Management and Decision-Making, History matching, Production forecasting, Risk, uncertainty, and risk assessment
Copyright 2018, Society of Petroleum Engineers
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