Analysis of Emissions Profiles of Hydraulic Fracturing Engine Technologies
Bibliographic record
Abstract
In the past twenty years natural gas production in the United States has significantly increased. This is largely due to technological advancements in unconventional methods of horizontal drilling and hydraulic fracturing. With these new and improved technologies, the United States has been able to increase its natural gas dry production per year by 14.5 trillion cubic feet per year from 2001-2021, a 74.1% increase. Horizontal drilling allows the wellbore to have more contact with the source rock, thus allowing more hydrocarbons to be extracted. Hydraulic fracturing allows oil and gas companies to have access to low permeable source rock, previously uneconomical to pursue. With hydraulic fracturing or “fracing” comes emissions from the heavy-duty engines used to power fluid pumps that drive the frac fluid into the ground. To evaluate the emissions from various engine types, a MATLAB model was developed and improved based on a model created by partner, BJ Energy Solutions. This model was also expanded by evaluating emissions for engines operating in the Marcellus shale play. The model was developed utilizing Environmental Protection Agency (EPA) standardized methodologies, data from previously conducted studies, as well as engine manufacturer data sheets. This model was created to accurately predict emission values and rates from greenhouse and non-greenhouse gases on fracing well sites. Seven engines were compared using the model: direct drive turbine, natural gas reciprocating, Tier 2 and Tier 4 diesel and dual fuel, and large turbine. Five shale plays were put into the model: Haynesville, Permian, Montney/Duvernay, Marcellus, and SCOOP/STACK (South Central Oklahoma Province/Sooner Trend Anadarko Canadian Kingfisher). A total of eight cases were ran with five varying parameters: shale play, pumping pressure, fluid flow rate, and Tier 2 and Tier 4 Dual Fuel substitution ratios. Each case assumed the same amount of pumping hours per day at 17 and only the Marcellus case differed in stage length. Each of the cases are equivalent to one days’ worth of fracing, a total of 17 pumping hours. For Cases 1 through 6 over all the engines the average total CO2e emissions was 2342 Metric Tons. The Titan direct drive engine had the best CO2e values at 21.7% less than the average and the Tier 2 Dual Fuel engine had the worst CO2e values at 32.9% greater than the average. Cases 7 and 8 were used to look at the effects of changing the substitution rate on dual fuel engines. Cases 7
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".