A Practical Study of the Influence of Drill Solids on the Corrosion of Downhole Tubulars When Using Brine Based Drilling Fluids
Bibliographic record
Abstract
Abstract The proliferation of highly concentrated brine drilling fluids systems due to their enhanced performance benefits has instigated a plethora of technical studies on the mechanisms and control of their induced corrosion on downhole drilling tools and tubulars. The majority of these studies often overlook the effect of drill solids on corrosion rates. Therefore, a pragmatic and experimental study was conducted to investigate the effects of various factors on the corrosion rates of downhole tubulars with a streamlined focus on the obscure role of the understudied drill solids; which have not been fully elucidated. Drill pipe corrosion coupons and drilling fluids/solids obtained from 5 similar wells (located in Grande Prairie, Alberta, Canada) were utilized for experimental analysis. Wells 1 to 4 were on the same pad (surface drilling location) drilling the same formation with the same fluid properties, while the 5th well was on a different pad but drilled the same formation with the same fluid properties to exclude disparity. Industry-standard measurement was carried out on the live used corrosion coupon rings, drilling fluids and solids obtained from these wells to determine selected properties. The total solids content analysis was carried out using an OFITE API (American Petroleum Institute) filter press. Weight loss tests on drill pipe corrosion coupons were used to determine field corrosion rates which were bolstered with the Parr Hastelloy autoclave test in the Laboratory. The oxygen content was monitored using Hach 2100Q dissolved oxygen meter. Field data, images and experimental results showed that a rapid and minuscule increase of drill solids (as little as 1% v/v) in the active system can impact corrosion rates greater than chemical additives and even oxygen content. It was discovered that low concentration of solids can produce significant damage and a high corrosion potential in non-viscosified fluids thereby making live monitoring of drilling fluids’ properties a priority to mitigate corrosion. This study fills an important technical gap in corrosion study that is indispensable for the optimization of corrosion control in drilling operations. By carrying out a controlled and investigative study backed up with drilling field data and images, the effects of the less understood drill solids have been partially demystified.
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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 | 0.001 | 0.001 |
| 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".