Pressure Monitoring to Detect Fault Rupture Due to CO2 Injection. (July 2017)

• Record Type: Journal Article
• Authors: Keating, Elizabeth; Dempsey, David; Pawar, Rajesh
• Is Part Of: Energy procedia. Volume 114(2017)
• Page Start: 3969
• Page End: 3979
• Publication Date: 2017-07
• Subjects: leak detection -- pressure monitoring -- fault rupture; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.03.1529
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: The capacity for fault systems to be reactivated by fluid injection is well-known. In the context of CO2 sequestration, however, the consequence of reactivated faults with respect to leakage and monitoring is poorly understood. Using multi-phase fluid flow simulations, this study addresses key questions concerning the likelihood of ruptures, the timing of consequent upward leakage of CO2, and the effectiveness of pressure monitoring in the reservoir and overlying zones for rupture detection. A range of injection scenarios was simulated using random sampling of uncertain parameters. These include the assumed distance between the injector and the vulnerable fault zone, the critical overpressure required for the fault to rupture, reservoir permeability, and the CO2 injection rate. We assumed a conservative scenario, in which if at any time during the five-year simulations the critical fault overpressure is exceeded, the fault permeability is assumed to instantaneously increase. For the purposes of conservatism we assume that CO2 injection continues 'blindly' after fault rupture. We show that, despite this assumption, in most cases the CO2 plume does not reach the base of the ruptured fault after 5 years. One possible implication of this result is that leak mitigation strategies such as pressure management [1] have a reasonable chance of preventing a CO2 leak. A very simple algorithm for detecting rupture, based on analysis of pressure data collected at the injection well, detects 100% of the ruptures. The algorithm is purely empirical and does not require a numerical or analytical model of the injection reservoir. However, this technique produces unacceptably high percentage of false positives (18%). We expect that in more realistic scenarios where multiple types of data are collected the rate of false positives would decrease. Regardless, our results suggest that for the class of scenarios we have considered the risk of undetected fault ruptures is low and the length of time available for mitigation is relatively long. The size of the 'rupture detection' footprint is significantly larger within the reservoir than in the overlying aquifers. In all layers the area near the fault zone is the first to respond and so would be easily detected if monitoring wells were placed near the fault. However, if the fault zone location is unknown a priori it will be unlikely to be intensively monitored and so these pressure changes may go undetected. Fortunately in the reservoir the pressure perturbation is also evident at the injection well and so the rupture can be detected even without an independent monitoring well network.