The Optimal Formula for Organic Oil Immobilization in Algerian Petroleum Drill Cuttings Using Solidification/Stabilisation Treatment
Bibliographic record
Abstract
Abstract Hassi Messaoud oil field is one of the most important fields in Algeria and the world, because it covers an important quantity of total Crude Oil Production in Algeria. Furthermore, two-thirds of this oil field is underexplored or not explored. Therefore, the drilling process of petroleum wells in this field is a continuous process that results in significant drilling waste. This implies that, enormous noxious quantities of drilling waste are produced daily that require treatment via solidification/stabilization (S/S) process before being landfilled. These types of wastes have pollution concentration that significantly exceeds the safety standards. In this study, we focus on the factors affecting the solidification/stabilization treatment of the drill cuttings obtained from Hassi Messaoud oil field and the process optimization. The solidification/stabilization is performed using the cement as binder, sand, silicate, organophilic clay and activated carbon as additives. The study is divided in two steps: (i) aims to determine the optimum ratio of each element used in the S/S process for the organic element (hydrocarbon) elimination, (ii) aims to combine the optimum ratios found in the previous step to determine the optimal mixture. The obtained results in the first step showed that the optimum ratio for the cement-to-drill cuttings mass ratio is 0.09:1. For the additives-to-drill cuttings mass ratio are 0.04:1, 0.006:1, 0.013:1 and 0.013:1 for the sand, sodium silicate, Organophilic clay and activated carbon, respectively. An optimum formula is found which its main finding shows that the hydrocarbon content of our sample is dropped from 9.40 to 1.999%. Many tests results before landfilling were investigated such as matrix permeability, resistance to free compression and heavy metals rate before and after S/S process. Besides that, in the light of outcomes achieved by this assessment these harmful cuttings can be converted into a useful product that helps in reducing the environmental foot prints.
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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 | 0.000 | 0.000 |
| 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".