HPHT 101-What Petroleum Engineers and Geoscientists Should Know About High Pressure High Temperature Wells Environment
Bibliographic record
Abstract
On April 20, 2010, BP’s Deepwater Horizon oil rig exploded in the Gulf of Mexico. This turned out to be one of the worst environmental disasters in recent history. This high-profile blowout at the Macondo well in the Gulf of Mexico, brought the challenges and the risks of drilling into high-pressure, high-temperature (HPHT) fields increasingly into focus. New Technology, HSE regulations, new standards, such as newly recommended procedures by the American Petroleum Institute (API), and extensive training programs for the drilling crew seem to be vital in developing HPHT resources. High-pressure high-temperature fields exist in Gulf of Mexico, North Sea, Southeast Asia, Africa and the Middle East. Almost a quarter of HPHT operations worldwide are expected to happen in the American continent particularly in North America. Major oil companies have tried to identify key challenges in HPHT development and production, and several service companies have offered many insights regarding current or planned technologies to meet these challenges. However, there are so many factors that need to be addressed and learned in order to safely overcome the challenges of drilling into and producing from HPHT oil and gas wells.Drilling into HPHT wells is a new frontier for the oil and gas industry. The growing demand for oil and gas throughout the world is driving the exploration and production industry to look for new resources. Some of these resources are located in deeper formations. According to US Minerals Management Service (MMS), over 50% of proven oil and gas reserves in the US lie below 14,000 ft. subsea. As we drill into deeper formations we will experience higher pressures and temperatures.Drilling operations in such high pressure and high temperature environments can be very challenging. Therefore, companies are compelled to meet or exceed a vast array of technical limitations as well as environmental, health and safety standards. This paper explains the technological challenges in developing HPHT fields, deepwater drilling, completions and production considering the reports from the Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE), formerly known as the Minerals Management Service (MMS). It reviews the HPHT related priorities of National Energy Technology Laboratory (NETL), operated by the US Department of Energy (DOE), and DeepStar Committees for Technology Development for Deepwater Research.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".