Evolution of Dual Fuel Pressure Pumping for Fracturing: Methods, Economics, Field Trial Results and Improvements in Availability of Fuel
Bibliographic record
Abstract
Abstract The displacement of a significant amount of diesel fuel with natural gas during large scale fracturing and drilling developments can be economically feasible in many cases with minor modifications to most diesel engines using dual fuel technology. Dual fuel describes the combination of a pressure and volume regulated stream of natural gas flowing through the engine intake and mixing with a reduced amount of injected diesel fuel in the engine to create power output that is nearly indistinguishable from using straight diesel fuel in high pressure fracturing pumps and other support equipment. Combining diesel fuel and natural gas in a dual fuel system reduces emissions of NOx, SOx, CO2, VOCs and particulates. Diesel fuel volume has been reduced by as much as 1,575 gallons per frac stage in field testing (63% of total fuel). Engine modification is with primarily bolt-on aftermarket and factory kits. Fuel savings vary depending on price and availability of the natural gas fuel source: LNG (liquid form) from existing liquefaction and/or storage infrastructure or CNG (gaseous form) from local distribution companies (LDC), and/or field gas or distribution gas pipeline systems. Learnings presented from field trials include a case history of frac jobs using CNG, LNG and other gas supply. Economic data is presented on the overall process. Recognition that availability of adequate CNG and LNG fuel supplies may be limiting expanded implementation of dual fuel technologies has prompted us to consider methods of making both fuel supplies more available to other industrial segments as well as the oil and gas industry.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".