Gyro Wellbore Placement Using Advanced Solid-State Sensor Technology
Bibliographic record
Abstract
Abstract This paper describes the construction of a wellbore that has been drilled using gyroscopic measurements alone to control the path of the well. Wellbores have not been drilled solely using gyroscopes since the early days of oil and gas extraction in the last century, and it is many years since a wellbore has been constructed without the use of magnetic MWD measurements. The exercise described here therefore marks a milestone in the application of downhole gyroscopic technology. The wellbore development described has been implemented using advanced solid-state gyroscopic sensors based on a new Coriolis vibratory gyroscope (CVG). Recent studies have demonstrated how this new sensor technology has become both a viable and practical option for high accuracy wellbore placement, largely achievable since CVGs are not affected significantly by the high levels of shock and vibration that can be experienced under extreme drilling conditions. The new technology provides an attractive option for while-drilling application, not only as an alternative to conventional gyro while drilling technology but also as a potential replacement for magnetic MWD. The results presented here illustrate the ability to use the gyroscopic measurements alone to achieve a planned wellbore trajectory. For the purpose of this case study, a post-drilling gyroscopic survey was conducted using a high accuracy gyro tool to verify the accuracy of the while drilling measurements. The paper sets out the numerous benefits that accrue from the application of CVG technology for the drilling of all types of well. First of all, it allows close proximity well placement in top-hole sections since the gyros are immune to magnetic interference from nearby wells. It also eliminates the need to add a nudge to the wellbore trajectory, a process that normally increases the tortuosity. The need to incorporate non-magnetic drill collars in the tool string is now removed, as well as the need for magnetic models and corrections, both with consequent cost savings. The new survey tool allows measurements to be taken near the bit facilitating more precise well path control. Multiple gyro sensor packages may be run simultaneously, thus providing independent surveys for quality control purposes and greater confidence in the detection of gross error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".