Well Completions and Profitability: A Horn River Shale Case Study
Bibliographic record
Abstract
Abstract The profitability of a well in an unconventional play is significantly influenced by its completion. It is widely understood that tighter rock needs more stimulation to economically recover hydrocarbons. However, how does one know if a well is being over-stimulated (fracture area created does not justify cost incurred) or under-stimulated (lost potential/profitability in productivity from a well's limited contact to the formation)? The objective of this paper is to develop and demonstrate an efficient workflow that will help stakeholders make better decisions in the area of completion planning. The workflow utilizes information from fracture modeling, production data analysis, and project economics to quantify the relationship between the key input parameters of the well completion (e.g. pumping rate, proppant and fluid pumped) and expected profitability expressed in net present value (NPV) terms. As a secondary objective, the case study demonstrates that a probabilistic approach (Monte Carlo Simulation) can be used to efficiently arrive at a consistent conclusion to the primary workflow. The output of the probabilistic model includes P90/P50/P10 production and net cash-flow forecasts, from which distributions of NPV can be obtained. This workflow is intended to help engineers compare profitability among different completion options. A shale gas field example is presented to illustrate the methodology.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".