Bibliographic record
Abstract
Abstract Drilling engineers can choose from a wide variety of tools and technologies, but designing the right drilling system may mean the difference between success and failure in many complicated applications. Every major oil-bearing area in the world comes with unique drilling challenges, which can be categorized in many ways. One grouping might be described as drilling the overburden or drilling the reservoir. Another grouping might be vertical vs. horizontal, and yet another might be exploration vs. development. The drilling challenge in any of these classifications becomes unique when it hinders the competitiveness of the project as it increases the risk and lowers the priority within an intra-company asset environment. Selecting the Right Alternative In recent years, the typical rotary steerable application has gone from unique to universal, but the underlying reasons for utilizing rotary steerable technologies are not always clear. Technically, many of the well paths and well designs can be drilled with conventional steerable drilling equipment, but a rotary steerable system is chosen because of the perceived benefits in drilling speed. If all the factors were considered, drilling speed or instantaneous rate of penetration (ROP) would be just one of several considerations, including reliability, trip frequency and trip time, hole cleaning considerations and short-trip frequency, hole quality and wellbore viability (stability). So, how are decisions being made to utilize one technology instead of another? One method is to search for the best average performance in an interval, among a wide range of wells. This fits with technical limit philosophies of many companies. Another, more practical method is to take into account all the variables that go into the various technologies and predict not just average ROP but an overall risked cost of the interval drilled with various methods. In directional wells, rotating and sliding drilling rates research needs to be weighed against failure rates and circulation times. Even hole quality and cuttings removal issues come into play when casing-running and primary cement success rates and short trips are considered. These models can be easily built, but collecting the data required to provide accurate and meaningful answers requires close cooperation between service company and operator. Figure 1 shows a simple set of input parameters and the economic impact of four potential drilling technologies in a specific interval. Based on the economic impact of various drilling technologies, a second-generation rotary steerable becomes the best economic option for rig rates greater than $100,000/day. Heavy Oil Reservoirs, Not Everyday Drilling Heavy oil reservoirs place some unique drilling challenges on operators, but with the right technology and personnel, the reservoirs can be drilled and produced successfully and economically. Several methods have been used to produce from heavy oil and bitumen reservoirs with varying degrees of success. Production methods fall into two categories: conventional heavy oil and enhanced recovery methods. Conventional heavy oil became more economical to produce with the advent of horizontal drilling, which became popular in Canada in 1989. Foamy oil recovery is the primary conventional heavy oil production technique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".