Revolutionizing Drilling: Integrating the Internet of Things (IoT) and Cloud Technology into Remotely Operated Managed Pressure Drilling (MPD) and Tripping in Canada, Duvernay Formation
Bibliographic record
Abstract
Abstract As the IoT continues to transform drilling operations, technologies that were once restricted to local control can now be remotely operated through a collective network that connects local devices with the cloud. This paper presents a case study where IoT integration enabled an operator to leverage the advantages of managed pressure drilling (MPD) operations in the Duvernay formation, all within a carefully planned and remotely controlled environment, leading to cost savings and more effective drilling operations. A hydraulics model was initially set up on an operating panel in the doghouse using MPD software, enabling direct remote access and integration with the rig's electronic drilling recorder. This allowed MPD equipment on-site to be operated locally or remotely. With the operating panel connected to the cloud, remote operators could manipulate MPD parameters before or during operations. Given that IoT and cloud technology were critical to safely operating the MPD equipment remotely, all personnel were briefed on the associated risks. Drillers received additional training to handle situations that prevented remote operators from controlling the equipment. Over the course of nine wells, constant bottomhole pressure (BHP) was remotely automated and applied to the wellbore, targeting anywhere between 1,750 to 1,900 kg/m³ at the landing point or Duvernay Top on connections during drilling. A little over 37,800 meters were drilled with MPD equipment operated remotely from Calgary and Houston. Remote operators also assisted with tripping operations, maintaining a dynamic target pressure at BH to mitigate swab effects while stripping out of the hole. To ensure the hydraulics model accurately reflected current well parameters, constant communication between on-site representatives, drillers, remote operators, engineers, and other vendors (e.g., drilling fluids, directional, and cementers) was recognized as an essential requirement, in which an integrated chat application and communication devices were all utilized. The success of drilling these nine different curves and laterals in the Duvernay, with no incidents, reflects the effectiveness of integrating IoT and cloud technology into traditionally manual operations, despite the challenges which were addressed through considerate pre-planning and execution. The use of IoT and cloud technology for remotely controlling MPD equipment is still uncommon in the drilling industry. This paper provides practical insights into the application of these technologies in actual drilling operations within the Duvernay formation. While it demonstrates the feasibility of IoT and cloud integration in MPD operations, it can also highlight potential future challenges and lessons learned in safely operating and controlling MPD equipment and software remotely
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".