Adding Extra Dimensions for Completion Consideration: Case Studies with Geoengineering, Measurement While Pumping, and Data Mining

Abstract

Well completion has evolved rapidly in the past two decades, as multistage completion has become the predominant practice to complete a well in many places. Although innovation in completion tool technology has been continuous in recent years, there are still gaps in the well completion optimization practice. In this paper, we add additional dimensions to well completion technology by incorporating geoengineering, measurement while pumping, and data mining, and we have evidence to show that those additional elements help to improve our understanding, on-site efficiency, and overall performance. Multistage completion optimization is about where and how to complete a well. Different methods were employed in the past, and even with a better-engineered completion design where both reservoir and completion quality are honored, there are still area for improvement. For example, 1) geological properties are not qualitatively utilized in the completion design; 2) real-time operational feedback during the execution phase is inadequate for in-time decisions for completion and fracturing adjustment; 3) the completion-to-well-performance cycle is so long that the learning curve is not fast enough, and too many influential factors are hidden in the details. Three extra dimensions were added to address the improvement areas. Geoengineering adds "space information" in enabling geological properties from a 3D space grid to be projected onto the wellbore as geology quality (GQ) so that the information can be used together with reservoir and completion quality (RQ and CQ) quantitatively to improve the fracturing treatment design. Measurement while pumping (MWP) adds "timely feedback" in that real-time operational feedback—either from the wellbore via high-frequency pressure monitoring or from the target zones via microseismic data in offset horizontal monitoring wells—can help with the completion and fracture diagnosis and decision making on-site. Data mining adds "pattern recognition" in that reservoir and operation data are collected and analyzed to generate a systematic understanding of the reservoir complexity, paving the way for the improved planning of future well completions in the same region. Each of the solutions comes with specific case studies in our work. Geoengineering, MWP, and data mining add three dimensions to the current well completion practice. In our case studies, these approaches have demonstrated the capability to improve the accuracy of the design, increase confidence in the execution, and accelerate the learning curve from evaluation. The extra dimensions added to the current completion practice are essentially space, time, and pattern, and together, they help to define the direction of future innovations for completion optimization.