Change Management Challenges Deploying a Rig-Based Drilling Advisory System
Bibliographic record
Abstract
Abstract Sophisticated drilling analysis software is available to provide drillers with advice on setting and modifying drilling parameters such as WOB, RPM, etc., but getting a driller to accept the recommendations of the software is still complicated. Additionally, it is not sufficient that a driller on one test rig accept the changes to the drilling techniques and modified workflows. The challenge is to scale across an operator’s mixed rig contractor fleet getting fleet-wide driller and stakeholder buy-in. The system used for this paper consists of a Rig-based Drilling Advisory System (RDAS) where new advisory information is displayed in the driller’s cabin running real-time pattern recognition algorithms to detect drilling dysfunctions. When a drilling dysfunction is encountered, a change in drilling parameters is suggested. Additionally, drilling parameters from offset wells are made automatically available for the driller’s use on the drilling screen. Through this process, we are entrusting the field personnel with a slightly higher level of technical responsibility. The team iteratively improved the system using feedback from drillers who used the RDAS. Two rigs were selected for testing on how the drillers and the wellsite supervisors utilize the system. Feedback from these two rigs pointed to the need for customization on a well by well basis. Working through the on-site drilling engineers on the test rigs, modifications were then made to the system to fine tune how they wanted the drilling advisory to behave. For example, a wellsite supervisor wanted the system to ignore mild stick slip in a short drill section - a rigid system with no customization could not provide an adequate solution. Being adaptable helped to improve the acceptance, as the driller now started seeing the advisory more as a support tool. Agile software development was critical to success and the rig personnel appreciated the quick modifications to the system. This also gave them confidence in the process, and made them more responsive to change. Comparing drilling performance from wells before and after deployment provided a way to quantify the benefits. Seven total systems were deployed over a five month period. The operator continues to deploy to additional rigs until all active rigs are covered. Change management is challenging. Many projects fail when this process is not properly executed. Presented here in detail is the process used by an operator to successfully implement a drilling advisory system across a mixed group of rig contractors. This process and the learnings presented in this paper serve as a case study for other companies embarking on such deployments.
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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".