Nanomaterial-Based Drilling Fluids for Exploitation of Unconventional Reservoirs: A Review
Bibliographic record
Abstract
The world’s energy demand is steadily increasing where it has now become difficult for conventional hydrocarbon reservoir to meet levels of demand. Therefore, oil and gas companies are seeking novel ways to exploit and unlock the potential of unconventional resources. These resources include tight gas reservoirs, tight sandstone oil, oil and gas shales reservoirs, and high pressure high temperature (HPHT) wells. Drilling of HPHT wells and shale reservoirs has become more widespread in the global petroleum and natural gas industry. There is a current need to extend robust techniques beyond costly drilling and completion jobs, with the potential for exponential expansion. Drilling fluids and their additives are being customized in order to cater for HPHT well drilling issues. Certain conventional additives, e.g., filtrate loss additives, viscosifier additives, shale inhibitor, and shale stabilizer additives are not suitable in the HPHT environment, where they are consequently inappropriate for shale drilling. A better understanding of the selection of drilling fluids and additives for hydrocarbon water-sensitive reservoirs within HPHT environments can be achieved by identifying the challenges in conventional drilling fluids technology and their replacement with eco-friendly, cheaper, and multi-functional valuable products. In this regard, several laboratory-scale literatures have reported that nanomaterial has improved the properties of drilling fluids in the HPHT environment. This review critically evaluates nanomaterial utilization for improvement of rheological properties, filtrate loss, viscosity, and clay- and shale-inhibition at increasing temperature and pressures during the exploitation of hydrocarbons. The performance and potential of nanomaterials, which influence the nature of drilling fluid and its multi-benefits, is rarely reviewed in technical literature of water-based drilling fluid systems. Moreover, this review presented case studies of two HPHT fields and one HPHT basin, and compared their drilling fluid program for optimum selection of drilling fluid in HPHT environment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".