Integrating Renewable Energy Behind-The-Meter In Upstream Oil and Gas Operations - Part II
Bibliographic record
Abstract
The cost of solar photovoltaic power has decreased over the past 15 years making it an attractive option for oil and gas companies to cost-effectively reduce their Scope 1 and 2 greenhouse gas emissions. This paper is the $2^{{\mathrm {nd}}}$ paper in a two-part series describing the integration of behind-the-meter solar PV in an upstream oil and gas operation, using a case study based mainly on solar power plants being installed in West Texas and New Mexico, as well as one commissioned in California in 2020. The first paper covered the feasibility, organization, early concept engineering, and project development of behind-the-meter solar power in an oil and gas operation. This paper discusses the engineering, design, commissioning, and initial operation of the solar farm. The authors explore the technical aspects specific to integrating these farms in operational oil and gas environments plus the economic and logistic challenges faced during execution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".