Successful Decarbonization of Drilling Fluids by Using Highly Efficient Technology and Reduced Chemical Usage, Logistics, and Casing Sections
Bibliographic record
Abstract
Abstract In accordance with the industry’s efforts to reduce its carbon footprint in all phases of operation, it has become vital to investigate innovative ways of reducing pollution to acceptable levels and to optimize drilling fluid usage and formulations. As the main us er of chemical products during the drilling phase, the drilling fluid has a significant effect on the carbon footprint, which is exacerbated when drilling fluid losses occur. An innovative technology has proven to be a very efficient sealant, displacing a number of conventional materials used to reinforce the wellbore while drilling. Thus, the use of calcium carbonate, graphite, Gilsonite, and asphalt, among other products, has been significantly reduced or in some cases, eliminated, from the formulation of specific drilling fluids, due to a technology that provides a thin but highly competent shield that prevents the invasion of fluid and pressure into the formation, thereby expanding the operative window. This technology is used in the drilling fluid, so that every freshly drilled rock is immediately sealed with flexible particles thus preventing the start of fluid invasion and wellbore washout. This technology enables the operator to reduce not only the amount of chemical products used in the drilling fluid, but also to save casing sections due to the increase in the operative window, prevent drilling fluid loss, reduce land and offshore transportation to the wellsite, and consequently reduce the supply chain process from the original source of the raw materials, manufactured products, to the end users at the rig site. The average total amount of chemical products utilized per well drilled has decreased by 34%, which has a significant impact on the supply chain, including transportation, associated logistics, and raw materials, among other components. In addition, each time a casing section is saved, the carbon footprint is lowered, saving casing section (steel production), cementing materials, wireline logging, rig days, logistics, and so on. This paper will discuss the technological strategy chosen to deploy this sort of technology, the results obtained in terms of operations efficiency, and an estimated calculation of carbon footprint reduction per operation completed. The materials previously used control potential drilling fluid losses were inflexible and sensitive to shear pressures, such as calcium carbonate, and their size was constantly changing, undermining their ability to form a dependable seal. In contrast, the focus of this research is selecting material that is flexible, making it resistant to shear forces. As it flexes to conform to the size and shape of the pore throat, the elastic capacity mitigates the uncertainties for the best design in a given particle size distribution pore throat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".