Bibliographic record
Abstract
Assuming no mathematical or chemistry knowledge, this book introduces complete beginners to the field of petroleum engineering. Written in a straightforward style, the author takes a practical approach to the subject avoiding complex mathematics to achieve a text that is robust without being intimidating. Covering traditional petroleum engineering topics, readers of this book will learn about the formation and characteristics of petroleum reservoirs, the chemical properties of petroleum, the processes involved in the exploitation of reservoirs, post-extraction processing, industrial safety, and the long-term outlook for the oil and gas production. The descriptions and discussions are informed by considering the production histories of several fields including the Ekofisk field in the North Sea, the Wyburn Field in Canada, the Manifa Field in Saudi Arabia and the Wilmington Field off the Californian Coast. The factors leading up to the well blowouts on board the Deepwater Horizon in the Gulf of Mexico and in the Mantara Field in the Timor Sea are also examined. With a glossary to explain key words and concepts, this book is a perfect introduction for newcomers to a petroleum engineering course, as well as non-specialists in industry. Professor David Shallcross is one of the foremost practitioners in chemical engineering education worldwide. Readers of this book will find his previous book, Chemical Engineering Explained, a useful companion.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.129 | 0.169 |
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".