Integrating Renewable Energy Behind the Meter in Upstream Oil and Gas Operations - Part I
Bibliographic record
Abstract
The cost of renewable power has decreased rapidly over the last 15 years, making investment in renewable energy an attractive way for any large power consumer to cost-effectively reduce scope 1 and 2 greenhouse gas emissions. As the oil and gas industry evolves to meet the challenges of the energy transition, including greenhouse gas reduction targets, the application of renewable energy resources behind the meter is a viable strategy to meet these needs. This paper intends to be the first of two discussions centered around the integration of renewable power in upstream oil and gas applications. The authors will discuss the process from feasibility evaluations, including power forecasting and greenhouse gas reduction estimates, to major barriers in selecting locations for development. A key technical challenge considered is integrating inverter-based resources to load serving substations. A case study based on a solar farm in West Texas, which has a relatively low cost of electricity, will be used as a model in this paper. Emphasis will be given to behind-the-meter renewable energy challenges highlighting the different economic incentives as compared to in front of the meter applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".