Carbon sequestration in oil reservoirs and deep saline formations may be accomplished by many different trapping mechanisms. Use of CO₂ for Enhanced Oil Recovery (CO₂-EOR) leads to CO₂ in three distinct phases, including CO₂ dissolved in oil, CO₂ dissolved in water (aqueous) and/or supercritical CO₂. We evaluated the total amount of stored CO₂ as well as the amount of CO₂ in each phase for an active CO₂-EOR site in western Texas.

Three-phase relative permeability and associated hysteresis are two major sources of model uncertainty. Both are difficult to measure and are usually predicted by interpolation models. Instead of using arbitrary interpolation models, we used a model-averaging method based on Bayesian inference to estimate integrated model uncertainty for 12 alternative models. Moreover, given the uncertainty of intrinsic rock properties including permeability and porosity, uncertainty quantification (UQ) of these parameters is also necessary for forecasting CO₂ storage capacity. Thus, results of this study provide uncertainty based on both model and data uncertainty.

Conventional Monte Carlo methods with geocellular simulations are computationally expensive. We applied a Polynomial Chaos Expansion (PCE) methodology instead, to reduce computational requirements while minimizing the loss of accuracy. Geostatistical techniques were applied to generate stochastic realizations based on well logs and seismic survey data.

For the Texas case study, we developed a systematic approach to quantify overall uncertainty, including both model uncertainty and parameter uncertainty. The approach was applied to forecast results at three important time steps, the end of the 30-year CO₂-EOR injection period, the end of the 20-year post EOR CO₂ injection period, and the end of the 50-year monitoring period. Results suggest that oil solubility dominates CO₂ trapping and aqueous solubility has the least relative importance with respect to trapping (storage). Predictions of model averaging preserved the general pattern and captured differences among alternative models. CO₂ storage of the reference model was within one standard deviation of predictions of model averaging. Estimated relative error between forecasted CO₂ storage and the reference model are 0.8%, 7.4%, and 6.1% at three selected time steps. By the end of simulation, estimated CO₂ storage in five selected layers in oil, supercritical, and aqueous phases are 3.4 ± 0.3 million tonnes, 2.0 ± 0.25 million tonnes, and 0.24 ± 0.04 million tonnes, respectively.

油藏和深层盐层中的碳固存可以通过许多不同的捕集机制来完成。使用CO的₂用于强化油采收（CO ₂ -EOR）导致CO ₂在三个不同阶段，包括CO ₂溶解于油中，CO ₂溶解于水（水溶液）和/或超临界CO ₂。我们评估了德克萨斯州西部一个活跃的CO ₂ -EOR站点中每个阶段存储的CO ₂总量以及CO ₂量。

三相相对磁导率和相关的磁滞是模型不确定性的两个主要来源。两者都难以测量，并且通常通过插值模型进行预测。代替使用任意插值模型，我们使用基于贝叶斯推断的模型平均方法来估计12个替代模型的集成模型不确定性。此外，考虑到固有岩石特性（包括渗透率和孔隙率）的不确定性，这些参数的不确定性量化（UQ）对于预测CO _2的储存能力也是必要的。因此，这项研究的结果提供了基于模型和数据不确定性的不确定性。

具有地质细胞模拟的常规蒙特卡洛方法在计算上是昂贵的。我们改用多项式混沌扩展（PCE）方法，以减少计算需求，同时最大程度地降低精度损失。地统计学技术被用于基于测井和地震勘测数据来生成随机实现。

对于德克萨斯州的案例研究，我们开发了一种系统的方法来量化总体不确定性，包括模型不确定性和参数不确定性。该方法已应用于三个重要时间步骤的预测结果：30年CO ₂ -EOR注入期的结束，EOR CO ₂注入后20年期的结束以及50年监测的结束时期。结果表明，油溶性在CO ₂捕集中占主导地位，而水溶度相对于捕集（存储）具有最低的相对重要性。模型平均的预测保留了一般模式，并捕获了替代模型之间的差异。一氧化碳₂参考模型的存储量在模型平均预测的标准偏差之内。在三个选定的时间步长处，预测的CO ₂储存量与参考模型之间的估计相对误差为0.8％，7.4％和6.1％。到模拟结束时，估计在油，超临界和水相的五个选定层中的CO ₂储存量分别为3.4±30万吨，2.0±25百万吨和0.24±0.04百万吨。