Intelligent Directional Survey Data Analysis to Improve Directional Data Acquisition
Bibliographic record
Abstract
Many countries rely on oil and gas production as it is an essential part of the global economy.As a result, various challenges may thrive from the process of extracting oil and gas from the ground that may affect the operational aspects of the construction process.So, it is important to maintain the production and Health, Safety & Environment (HSE).The project aims to automate the process of the Directional Survey Data (DSD) in a way that can be cost-effective for the operational process and more stable for future use.DSD relates to the process of horizontal directional drilling (HDD) and raw data obtained from the surveys using survey stations on the way to bore hole like azimuth and inclination etc.In this work, we propose a fully automatic Directional Survey Data Analysis system based on the recognition patterns.The dataset comprised of 34069 real-time instances has been used.Two machine learning algorithms and four deep learning algorithms were investigated in this regard.For the deep learning approach RNN, LSTM, BI-LSTM, and Extreme Learning Machine (ELM) were used, and for the machine learning approach SVM and Naï ve Bayes have been investigated.Selection of these candidate approaches was based on their promising nature in the related fields of study in terms of accuracy and precision.The experimental result demonstrated that Naï ve Bays got 100% accuracy, ANN, LSTM and GRU managed to get 100% accuracy, BI-LSTM had a slightly lower accuracy achieving 98.7%, Simple RNN was lower than BI-LSTM achieving 82% accuracy, SVM got 81.1% accuracy, while ELM had the lowest performance receiving 55.3% accuracy.Overall, the scheme outperforms state-of-the-art techniques in the literature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".