<p> Analytical models were derived in this work to predict the Maximum Permissible Pressure (MaxPP) and Minimum Permissible Pressure (MinPP) in CO<sub>2 </sub>sequestration and other fluid injection wells. The outer radius of the cement sheath should be estimated on the basis of cement placement efficiency measured by the CBL. </p><p> The West Hastings Oil Field and Oyster Bayou Oil Field in Gulf of Mexico region were analyzed to identify the potential leakage of the current CO<sub> 2 </sub> injection wells using the analytical models. Potential problems for the current wells were identified. There are potential risks for the CO<sub> 2 </sub> injection wells with relatively smaller wellbore diameter and casing diameter.</p><p> 36 CO<sub>2 </sub> injection wells of the West Hastings and Oyster Bayou fields were taken as learning wells to train the neural network model, which was tested by 21 wells in the fields. The results show that the neural network model could be used for predicting the potential likelihood of leakage for CO<sub>2 </sub> injection wells, which could be an alternative and convenient way to assess the risk of leakage CO<sub>2 </sub>.</p><p> Sensitivity analysis was also performed considering cement mechanical properties, well structure and reservoir pressure. Results show that improving cement sheath mechanical properties (cement tensile strength, cement cohesive strength, internal friction angle) is not a very effective means of decreasing potential leakage of CO<sub>2 </sub> during CO<sub>2 </sub> EOR and carbon sequestration processes. The potential risk of leakage for CO<sub>2 </sub> injection wells should be decreased by maximizing the outer radius of the cement sheath and improving the cement placement efficiency. For the current CO<sub>2 </sub> EOR activities and carbon sequestration processes, the well head maximum water injection pressure could be increased as the reservoir pressure increases.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:1557563 |
| Date | 25 July 2014 |
| Creators | Li, Ben |
| Publisher | University of Louisiana at Lafayette |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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