Application & Evolution of Formation Pressure While Drilling Technology (FPWD) Applied To The Gulf of Mexico
Bibliographic record
Abstract
Abstract The emergence of formation pressure while drilling (FPWD) technology into the Gulf of Mexico (GoM) in 2004 was a step change for the industry. Over time the technology has been accepted by the industry as a reliable source for formation pressure and mobility measurements in real time for both reservoir evaluation and drilling optimisation. As the drilling frontiers are pushed, FPWD technology has advanced to meet the challenges. What have we learnt from this journey? Today, the technology is applied in an increasingly diverse environment in the GoM with a focus on safety and data quality. This has been achieved by a perpetual learning and development cycle, advancing FPWD technology, logging while drilling (LWD) technology and refining processes. Advances in Real Time capabilities in LWD have also become critical to allow fast and founded decisions. Examples are presented reviewing the applications and associated development of the technology over time to meet and exceed the GoM challenges: Ultra deepwater, high pressure exploration in pressures exceeding 27k psi Reservoir evaluation in the heterogeneous Wilcox formation, where mobilities can swing from 0.5 to 50 mD/cP in a matter of inches, result in poor quality or long stationary times when fixed rates and volumes are applied blindly; however integration of a intelligent pretest design that automatically determines optimal rates and volumes in milliseconds can turn failure into success Sub salt drilling optimisation and pore pressure calibration Testing in severe pore pressure regression and/or highly depleted reservoirs with overbalances in excess of 7k psi. Refining electronics and pretest motor controls opened up new opportunities for operators to determine magnitudes of depletion previously unobtainable Shallow, unconsolidated or highly overpressured formations, which can lead to lost seals as the effective stress increase to the point of sand face failure, unique test designs perform low shock tests against the formation Well to well comparisons for vertical and lateral connectivity required pressure gauge development in high pressure environments >20k psi High spread rates in deepwater GoM require the efficient application of these new technologies. Through application of lessons learnt and technology advancements, Non Productive Time (NPT) can be minimized and data quality maximized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".