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Assessment of Eagle Ford Shale Oil and Gas Resources

机译:伊格福特页岩油气资源评估

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摘要

The Eagle Ford play in south Texas is currently one of the hottest plays in the United States. In 2012, the average Eagle Ford rig count (269 rigs) was 15% of the total US rig count. Assessment of the oil and gas resources and their associated uncertainties in the early stages is critical for optimal development. The objectives of my research were to develop a probabilistic methodology that can reliably quantify the reserves and resources uncertainties in unconventional oil and gas plays, and to assess Eagle Ford shale oil and gas reserves, contingent resources, and prospective resources. I first developed a Bayesian methodology to generate probabilistic decline curves using Markov Chain Monte Carlo (MCMC) that can quantify the reserves and resources uncertainties in unconventional oil and gas plays. I then divided the Eagle Ford play from the Sligo Shelf Margin to the San Macros Arch into 8 different production regions based on fluid type, performance and geology. I used a combination of the Duong model switching to the Arps model with b = 0.3 at the minimum decline rate to model the linear flow to boundary-dominated flow behavior often observed in shale plays. Cumulative production after 20 years predicted from Monte Carlo simulation combined with reservoir simulation was used as prior information in the Bayesian decline-curve methodology. Probabilistic type decline curves for oil and gas were then generated for all production regions. The wells were aggregated probabilistically within each production region and arithmetically between production regions. The total oil reserves and resources range from a P_(90) of 5.3 to P_(10) of 28.7 billion barrels of oil (BBO), with a P_(50) of 11.7 BBO; the total gas reserves and resources range from a P_(90) of 53.4 to P_(10) of 313.5 trillion cubic feet (TCF), with a P_(50) of 121.7 TCF. These reserves and resources estimates are much higher than the U.S. Energy Information Administration?s 2011 recoverable resource estimates of 3.35 BBO and 21 TCF. The results of this study provide a critical update on the reserves and resources estimates and their associated uncertainties for the Eagle Ford shale formation of South Texas.
机译:目前,德克萨斯州南部的鹰福特(Eagle Ford)戏剧是美国最热门的戏剧之一。 2012年,Eagle Ford钻机的平均数量(269台)为美国钻机总数量的15%。早期评估油气资源及其相关的不确定性对于优化开发至关重要。我研究的目的是开发一种概率方法,可以可靠地量化非常规油气田中的储量和资源不确定性,并评估Eagle Ford页岩油气储量,或有资源和预期资源。我首先开发了一种贝叶斯方法,使用马尔可夫链蒙特卡洛(MCMC)生成概率下降曲线,该曲线可以量化非常规油气田的储量和资源不确定性。然后,根据流体的类型,性能和地质情况,我将Eagle Ford的油田从Sligo Shelf Margin扩展到San Macros Arch,分为8个不同的生产区域。我使用在最小下降率下将Duong模型转换为b = 0.3的Arps模型的组合来对在页岩气层中经常观察到的线性流到边界支配的流态进行建模。贝叶斯下降曲线方法将蒙特卡罗模拟和储层模拟相结合预测的20年后的累计产量用作先验信息。然后针对所有生产区域生成了石油和天然气的概率类型下降曲线。在每个生产区域内以及算术生产区域之间以概率方式聚集孔。石油储量和资源总量从5.3的P_(90)到287亿桶石油(BBO)的P_(10),P_(50)为11.7 BBO;天然气储量和资源总量从P_(90)为53.4到P_(10)为313.5万亿立方英尺(TCF),P_(50)为121.7 TCF。这些储量和资源估算远高于美国能源信息署(US Energy Information Administration)2011年的3.35 BBO和21 TCF的可采资源估算。这项研究的结果为南德克萨斯州Eagle Ford页岩地层的储量和资源估计及其相关的不确定性提供了重要的更新。

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    Gong, Xinglai;

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  • 年度 2013
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  • 正文语种 en
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