Tight Oil Field Development Optimization Based on Experience of Canadian Analogs
Bibliographic record
Abstract
Abstract The paper presents a study of field development optimization of the large tight oil field in West Siberia. The field is at an early development stage and is characterized by low permeability (less than 1 mD). It is developed by horizontal wells with multistage hydraulic fracturing. Analysis of available information about the field revealed the potential to improve field development efficiency. Field development analysis and optimization were carried out based on the experience of development of similar Canadian reservoirs. Two large fields were selected as analogues: Bakken ViewField and Pembina Cardium. The data on these fields is publicly available. These fields are developed during a long period of time enabling operating companies to learn from experience and use new knowledge and data to optimize completions systems and development strategies as a whole. Therefore it is possible to not only analyze the current field development stage, but also trace the evolution of approaches and assess, what benefits can be obtained from making various changes to the applied technologies and field development strategy. The positive experience of development of the Canadian fields formed the basis for the field development optimization options. A set of suggested project decisions will enable improvement in field development efficiency and, in case of confirming by pilot projects, can be recommended for full-field implementation in the considered field and in the analogue fields.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".