Reservoir and production engineering processes involve a number of components applied to monitor and plan such parameters during oil and gas fields development as oil production profile calculation and ultimately recoverable reserves estimation. Field-statistical models such as displacement curve and decline curve analysis models are wildly used to forecast production performance and estimate reserves. As compared to decline curve analysis, displacement curve analysis provides a better result in production forecasting in waterflooded oil reservoirs since, apart from oil production, they also take into account liquid and water production. However, both methods are deterministic i.e. make no allowance for uncertainty in calculations that can result in failure to achieve planned production performance. In order to solve the problem of uncertainty quantification and risks assessment in production forecasts and ultimately recoverable reserves estimates, foreign scientists have applied methods of machine learning with traditional methods of oil and gas production forecasting.
This research for the first time applies such advanced machine learning methods as Bayes theorem and Markov Chain Monte Carlo simulation with integral displacement curve models for quantitative assessment of risks and uncertainty in the production forecasts and estimates of ultimately recoverable reserves. Developed methodology of probabilistic production forecasting yields to different probabilistic scenarios of production profiles and reserves estimates, accuracy improvement of the forecasts, and, as a consequent, increase in quality of decision making. In the framework of this study, hindcast has been carried out based on the history of 130 production wells of two waterflooded oil fields in order to assess reliability of the developed probabilistic methodology of production forecasting. In addition, the comparative study of developed method and foreign probabilistic methodology has been conducted on the basis of hindcast results.
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