Autonomous Drilling Platform - An Enabler for Automated Steering Control and Remote Operations in the Permian
Bibliographic record
Abstract
Abstract The Autonomous Drilling Platform (ADP) revolutionizes directional drilling by allowing drillers to concentrate on performance-driven tasks rather than routine calculations and activities. The ADP enhances the communication workflow between geosteering and downhole rotary steerable steering settings by employing a systematic approach that integrates new targets, calculates feasible trajectories, and assesses their viability within the constraints of the well design. This seamless integration and automation significantly improve the drilling and steering efficiency of the directional drilling operations. The innovative platform enables real-time wellplan updates, effectively bridging the communication gap between surface parameters, drilling engineering, with its tool limitations and downhole tools automation. The platform facilitates a dynamic and prompt response to changing subsurface conditions, thereby optimizing drilling efficiency and accuracy. The integration of automated calculations and synchronization between surface and downhole automation systems allows field engineers to focus on performance-oriented activities. ADP automatically downlinks the updated directional drilling settings to the downhole tools to execute the revised trajectory. It also provides operators with remote access via a web interface, enabling the monitoring of details pertaining to directional drilling operations from any location worldwide. The ADP was implemented in phases for varying well complexity in the Permian operations in Midland and Delaware Basins. Consistent drilling performance has been a challenge due to inconsistencies in drilling practices, steering control and sub optimal drilling parameters for the target formations drilled. ADP enabled the execution team to focus on drilling performance with an interface that encompasses drilling parameters along with steering control recommendations. The integration of a data workflow enabled remote operations reducing personnel on site while experienced team members execute jobs remotely. With ADP implemented above 80% of the drilling interval showcased better drilling performance and service quality compared to similar complexity wells that had no ADP or lower utilization of ADP during job execution. This innovative platform not only optimizes the drilling process but also enhances safety and reduce costs, marking a new era in the field of directional drilling business This paper describes the Autonomous Drilling Platform which represents a significant advancement in directional drilling technology. That is done by improving communication workflows, integrating automated calculations, and enabling remote operations. The ADP allows field engineers to concentrate on enhancing drilling performance and efficiency. As the industry continues to evolve, the ADP will play a crucial role in driving advancements and setting new standards for operational excellence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".