Optimizing Drilling by Simulation and Automation with Big Data

Abstract

For many years the aerospace and automotive industries have realized significant improvements in efficiency, performance and cost savings by simulating multiple prototype vehicle designs and control systems under various operating conditions. These same simulation techniques have now been introduced to the oilfield drilling industry and are delivering insights for more effective drilling tool designs, bottom hole assembly optimization, drilling severity minimization, dysfunction recognition and for drilling performance improvement with fewer downhole failures. Drilling is a non-linear, coupled and dynamic hydro-geomechanical process, the physics for all aspects of which must be captured to enable a robust automated drilling control process. Drill string, drilling tool and drill bit failures are frequently incorrectly blamed upon the invisible geology through which they drill. Field engineers frequently report more severe downhole vibrations at rotation speeds other than those predicted by linear frequency-based finite element critical speeds analyses. The same multi-body dynamics simulation techniques used by the automotive and aerospace industries, however, are now being applied to capture the non-linear aspects of the drilling process and provide more realistic predictions of drilling performance. Simulation validation is achieved by comparing virtual data to physical data with an implicit understanding of the uncertainties of each. Recommendations are presented for improving the usefulness and the quality of physical drilling data which simulation can then also help assure. The ultimate objective is to deliver better quality boreholes which are less costly with fewer drilling tool failures. These novel simulation techniques are enabling manufacturers to benefit from lower development costs and shorter times to market with more reliable proprietary drilling tool designs. Drilling contractors are using simulations to optimize top-drive controls and drill more effectively. Product developers are able to configure higher performing and more optimal bottom hole assemblies. Operators are able to reduce overall drilling costs with the potential benefits of higher performing drilling automation systems and greater production from better quality boreholes.

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

“…We have to build large open-source datasets for mineral data analytics research to explore more. Through drilling simulation, virtual and physical data and parameters can be compared and uncertainty can be determined [43]. As a result, cost optimization is possible.…”

“…Seismic interpretation [30], analysis [19], Drilling knowledge discovery [68], [83], optimization [43], Drilling cost optimization [77], [34] Production engineering [90], production allocation [21], well productivity [55], surveillance [114],…”

Human Centric Digital Transformation and Operator 4.0 for the Oil and Gas Industry

“…The invention, improvements and application of new data recording tools (e.g., seismic acquisitions devices, channel counting, fluid front monitoring geophones, carbon capture and sequestration sites, logging while drilling), measurement and data formats have made it even more applicable to employ big data analytic tools in drilling operations [100]. In this area, big data analytics can be deployed to improve the drilling performance [105], find invisible non-production time [106], and reduce the risks associated with drilling operations, the risks of drilling failures, and can also lower drilling development costs [107], [108]. A report done by B. Marr, [109] noted that Shell uses big data analytics in near realtime to detect failures.…”