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Probabilistic Production Forecasting and Reserves Estimation in Waterflooded Oil Reservoirs

机译:水柴油储层概率生产预测及储备估算

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摘要

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 allow engineers to estimate reserves and forecast production performance in waterflooded oil reservoirs taking into account either liquid or water production. Compared with Decline Curve Analysis (DCA), WDC models are expected to perform better in forecasting oil production in waterflooded oil fields. In this study I applied Bayesian methodology and Markov Chain Monte Carlo (MCMC) methods with WDC models. 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 methods. 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 ? WDC Multimodel, MCMC with WDC and MCMC with DCA are well-calibrated probabilistic methods? WDC Multimodel performs more than 20 times faster than MCMC with WDC and MCMC with DCA techniques having the same level of reliability ? Compared with MCMC using DCA, WDC Multimodel and MCMC with WDC show more reliable results when the history matching period is less than 24 months Computer software was developed during this research to make the process of calculations more convenient.
机译:不确定量化和石油行业风险评估的重要性不能夸大。在生产预测和储备估计中,将始终存在不确定性。在估算项目的储备和盈利能力时低估不确定性会导致决策差和失望。水位曲线(WDC)模型允许工程师估算水上油藏的储备和预测生产性能,同时考虑到液体或水生产。与下降曲线分析(DCA)相比,预计WDC车型在水上油田中预测石油生产方面会更好。在这项研究中,我用WDC模型应用了贝叶斯方法和马尔可夫链蒙特卡罗(MCMC)方法。我还通过11 WDC模型开发了一种多模型方法,通过评估每个模型提供的匹配和预测的差异来量化生产预测的不确定性。使用WDC型号进行校准多模型和MCMC,并与MCMC进行DCA方法进行校准。使用来自实际水柴油油田的100个井的生产历史评估了开发方法的可靠性。我表演了Hindcast研究,其中我认为,实际的历史生产数据的一些部分是已知的(6,12,24和36个月),其余的实际生产是未知的(5-7岁)。然后,我将假定的已知生产分数与历史上的假定的生产分数相匹配,并预测生产到实际历史时期结束。在HindCast结束时的累积产量与实际累积生产进行比较,以测试生产历史限制的方法的概率可靠性。这项研究表明了吗? WDC MultiModel,带有WDC和MCMC的MCMC具有良好校准的概率方法吗? WDC MultiModel比使用WDC和MCMC的MCMC执行超过20倍,具有具有相同可靠性水平的DCA技术?与MCMC使用DCA,WDC Multimodel和MCMC与WDC显示出更可靠的结果,当历史匹配期限在这项研究期间开发了计算机软件,使计算过程更方便。

著录项

  • 作者

    Maksim Nazarenko;

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  • 年度 2018
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  • 原文格式 PDF
  • 正文语种 eng
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