Data-driven prediction of drilling strength ahead of the bit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper compares the performance of two data-driven methods, Signal-Matching Predictor (SMP) and Long Short-Term Memory (LSTM), for predicting drilling strength (E s ) ahead of the bit based on drilling data from nearby offset wells. The comparison is based on the accuracy, applicability, complexity, and computational cost of the methods with the objective of suggesting the most appropriate tool for look-ahead drilling strength prediction. The methods were tested using data from offshore wells in Newfoundland. The SMP used a fixed-size sliding-window of real-time E s data from the target well to find a match in the offset well within similar geological formations and chose the scaled value from the offset well as the prediction. In the second approach, twelve LSTM models were trained using the drilling data of twelve offset wells, and the drilling data of the thirteenth well was used for blind testing. Results showed that the SMP achieved a coefficient of determination ( R 2 ) of 0.92, 0.92, and 0.79 for predicting 1.5, 3, and 5 feet ahead of the bit, respectively, while the LSTM reached an R 2 of 0.95, 0.92, and 0.80 for the respective prediction intervals. The R 2 of the LSTM models was further increased to 0.96, 0.94, and 0.83 after retraining it with weighted samples in formation transition zones. Also, a post-processing technique was proposed that further enhanced the R 2 of the LSTM-based approach to 0.98, 0.97, and 0.93, respectively. The strength of the LSTM-based approach was to use measurable drilling parameters as the only inputs and not the E s itself. According to the results, the LSTM-based method can be reliably used to predict the E s ahead of the bit allowing drillers to identify upcoming drilling dysfunctions. • Signal Matching Predictor and LSTM predicted the drilling strength ahead of the bit. • LSTM was trained with drilling data of offset wells rather than drilling strength. • LSTM reached R 2 values of 0.98, 0.97, and 0.93 for 1.5, 3, and 5 ft prediction lags. • SMP's maximum R 2 was 0.92 for prediction at 1.5 ft ahead of the bit. • Proposed method can be used in real-time to enhance drilling and avoid dysfunctions.
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.
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 | 0.001 |
| 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 it