Technology Focus: Production and Facilities (December 2018)

Abstract

Technology Focus Creativity and innovation have long characterized production and facilities, and this year is no exception. Much of the work reported this past year was conducted during the recent period of low oil prices. The economic challenges of the oil industry clearly have provided a strong stimulus for even more creativity and innovation.  The use of big data and analytics appeared in a number of papers with an emphasis on the use of artificial intelligence (AI) for building databases used to monitor the health of equipment and structure risk-based-inspection (RBI) strategies. Monitoring data inputs from thousands of sensors (paper OTC 28990) allows an AI application to predict an impending failure and notify operators by text or email when the incipient problem is detected so that proactive maintenance can be scheduled to avoid an unplanned shutdown or catastrophic failure. This strategy is being successfully applied to compressors but no doubt will be used to monitor other high-cost, critical service equipment as well (paper SPE 188803).  Progress continues on the design and application of inflow-control devices (ICDs).  Introduced only a few years ago, these devices are still in a rapid development stage for both design and application. Now, ICDs are applied successfully to improve the fluid-injection patterns for both steamfloods and waterfloods, the latter being described as a successful field application (paper SPE 189824). For steamfloods, passive and autonomous ICD designs were evaluated and their performance modeled using computational fluid dynamics (paper SPE 189721). The integration of subsurface modeling and surface-facility design by the development of a data-driven stochastic work flow (paper SPE 187462) demonstrated a means to reduce both subsurface and facility costs by reducing the biases that inevitably come into play during the generation of a field-development plan.  Other innovative work was reported on field optimization, the prediction of asphaltene precipitation, and the integration of RBI with vibration-induced fatigue failure of installed piping systems. Interesting work not discussed here includes evaluating corrosion under severe conditions and the development of an oil-droplet-coalescing pump for use in water treatment. Recommended additional reading at OnePetro: www.onepetro.org. SPE 188803 Machines Performance Algorithmic Modeling for Anticipating Machines Health Using Real-Time Condition-Monitoring Data by W. Almadhoun, ADMA-OPCO, et al. SPE 189721 Evaluation of Inflow-Control-Device Performance Using Computational Fluid Dynamics by M. Miersma, University of Alberta, et al. SPE 187256 Production Optimization of Shenzi Field in the Deepwater Gulf of Mexico by P. Ashton, BHP, et al. SPE 190149 A Diagnostic Approach To Predict Asphaltene Deposition in Reservoir and Wellbore by Davud Davudov, University of Oklahoma, et al. OTC 28352 Integrating an RBI Approach for Vibration-Induced Fatigue Into a Mechanical-Integrity Program by Paul Crowther, Wood, et al.