Importance of Fluid Rheological Characterization on Managed Pressure Drilling Operations
Bibliographic record
Abstract
Abstract Determination of parasitic pressure losses has vital importance in selecting proper mud weight and estimating accurate equivalent circulating densities for managed pressure drilling operations (MPD). In most cases, non-Newtonian fluid models and parameters determined at surface conditions are used to calculate frictional pressure losses throughout the wellbore. Since synthetic based drilling fluids' rheological properties are sensitive to downhole conditions, discrepancies between measured and calculated pressure losses are observed. Prediction of the fluid's rheology under wellbore condition is fundamental for proper hydraulic programs' design. Only after analyzing the effect of pressuretemperature on fluid rheology, the gap between calculated and measured frictional pressure losses can be reduced. This article gives some background on MPD and its importance to overcoming certain challenging drilling operations. Since MPD will rely on close control of bottom hole pressure and equivalent circulating density, it is demonstrated the importance of accurately estimate the behaviour of the drilling fluid under downhole pressure and temperature conditions which can, for certain fluids, be considerably different from regular surface laboratory measurements. Introduction Development of Managed Pressure Drilling (MPD) technology has become of major importance for the industry in recent years. With the drilling industry facing progressively more challenging situations such as ultra-deepwater drilling, "narrow margin" drilling in depleted zones, high pressuretemperature (HPHT) wells and long reach wells, MPD is being faced as an alternative to conventional drilling that can successfully overcome those challenges and at the same comprise with rigid environmental regulations. MPD can be considered as a non-underbalaced application of underbalanced drilling (UBD) technique. Using the same tools as in UBD, which will include a rotating control device and an automatically operated choke, the technique will require the well to be drilled in a closed circulation system allowing close control of circulating pressure and equivalent circulating density at all times. As such, use of managed pressure drilling has the following potential benefits1,2:Improved wellbore stability;Reduced number of casing strings due to the capability of drilling extended hole sections;Substantial risk reduction while drilling in areas:with unknown pore pressure;with narrow margin between pore pressure and fracture resistance;environmentally sensitive;Reduced lost circulation and differential sticking problems;Operation can be easily converted to conventional drilling system if necessary;Reduced formation damage;Substantial reduction on the risk of kicks and blowouts;Savings on drilling time. Besides the above mentioned benefits, MPD will also allow drilling in certain areas, such as those found on deep and ultradeep water environment where it is estimated3 that one half of all offshore resources of oil and gas can not be explored economically using conventional drilling techniques and technology. As showed previously in Ref. 1, the maximum benefit of MPD will be obtained when bottom hole pressure is kept as close as possible to the pore pressure curve. This feature however only will be possible with a deep knowledge of the fluid behavior under downhole conditions.
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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 | 0.002 |
| 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 | 0.001 | 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".