Leveraging Run-Life Data and Industry Collaboration in a Low-Cost Environment

Abstract

ESP operators are continually striving to reduce downhole equipment failure rates in order to maximize well uptime, which results in reduced well servicing costs and maximized production. ESP system run-life data tracking and analysis activities are critical for determining appropriate actions that can be taken towards these objectives. These activities have typically included collecting, validating and storing data for a relatively large set of parameters. However, in low-cost operating environments, comprehensive data collection activities are often ranked as a relatively low priority. This paper proposes a strategy to leverage a relatively smaller data collection effort and high-level analysis, covering only a limited number of critical run-life parameters, to narrow down the most severe reliability challenges. Additional efforts can then be focused on internal and collaborative activities specific to addressing the top challenges, therefore reducing total operating costs. The proposed strategy is based on work conducted within a joint industry project. This paper discusses two case studies where this strategy was successfully implemented. The first case study relates to the challenges associated with installing and operating ESP systems in horizontal wells and wells with regions of high curvature. Once substantially higher ESP equipment failure rates were confirmed for wells with regions of high curvature, a focused effort by several companies was initiated to correlate equipment failure rates with more detailed well and equipment characteristics to properly establish reasonable operating envelopes. The second case study relates to the identification of a critical equipment sub-component with a relatively high failure rate in certain applications. Upon the sub-component being identified, a focused industry effort was initiated through lab testing to determine which designs may be more robust, given the specific demands of the application. The above case studies have shown that, by structuring the data collection process to include an initial limited data collection effort, key challenges can be effectively identified and then limited available resources can be leveraged through industry collaboration to undertake actions that have the potential to lower operating costs and increase production.