Unconventional Well Profitability vs. Asset Profitability
Bibliographic record
Abstract
Abstract The reserves growth in unconventional resources over the last three years has taken everyone by surprise and the ramifications are only now being fully realized by the marketplace and government. The role of shale basins in this growth has forever changed the skill expectations of oil industry professionals. This growth, primarily focused in North America today, will almost assuredly spread globally. The economics of unconventional resource plays are quite different from those of conventional plays. Even the business metrics of success are usually ranked differently (Roundtree et al, 2009). This paper examines the overall cost structure of unconventional plays (land, drilling, completion, and well operation) versus the potential return. Prudent application of front end exploration to enter a play under favorable land price conditions, pragmatic well construction and completion practices to control costs, and efficient development drilling programs to optimize the resource recovery factors must all be considered in overall resource play economics. We demonstrate how irrational pursuit of lower development costs can often be detrimental to overall play economics and ultimate resource recovery. Investor and governmental pressure may grow as these stakeholders segregate the operators who excel in achieving a balance of the different factors to sustainably produce better economic returns for their shareholders while extracting the maximum recoverable resource from the reservoirs in question. Stranded reserves will be seen ever more so as a measure of engineering malfeasance and not just sub-optimum economics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".