A New Low-Damage Drilling Fluid for Sandstone Reservoirs With Low-Permeability: Formulation, Evaluation, and Applications
Bibliographic record
Abstract
Abstract Drilling-induced formation damage is the key factor dominating the failure of the development of hydrocarbon reservoirs with low-permeability (i.e., tight formation). In this paper, a new low-damage drilling fluid was formulated, evaluated, and applied to well-drilling operations in a sandstone oil reservoir with low-permeability in the Shengli Oilfield, China. To formulate this low-damage drilling fluid, filter-cake forming agents were used to prevent fluid loss, inhibitors were used to enhance the shale inhibition of the fluid, surfactants were used to minimize water block, and inorganic salts were used to enhance compatibility. A holistic experimental approach combining micro-computed tomography (CT), scanning electron microscopy (SEM), Fourier transform-infrared spectroscopy (FT-IR), and X-ray diffraction (XRD) techniques was designed to identify the underlying interactions between new and conventional drilling fluids and rock samples as well as the corresponding damage mechanisms, demonstrating the significant mitigation effects of the newly formulated drilling fluid on formation damage, which mainly results from the hydration of clay minerals and the invasion of solid particles. The newly formulated low-damage drilling fluid then extended its applications to well-drilling operations with excellent performance. Not only can the new low-damage drilling fluid avoid non-fracturing stimulation, but also reduce the drilling operational costs and time, minimize the formation damage, and facilitate extending the reservoir life for a longer time.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".