Changing Drillstring Design to Allow Accurate Calculation of BHA SAG Corrections
Bibliographic record
Abstract
Abstract Steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) drilling environments in Northern Alberta have some of the tightest geological control in the world. In many cases, there are multiple stratigraphic wells along the length of the vertical section of the horizontal legs. This geological control allows wells to be more accurately positioned with formation evaluation sensors than in most areas, which often means that the resulting wellbore position from directional surveys, containing compounding survey uncertainty, places the wells differently than expected from the geological wellbore positioning. Through empirical testing in controlled environments and field validation trials, it was determined that small changes in the bottomhole assembly (BHA) design can greatly reduce the deflection of BHA components and the impact it has on the misalignment of the directional sensor, otherwise known as BHA SAG. These small changes to the BHA allow the current industry modeling software to more accurately predict the BHA deflection, providing the most accurate survey inclination corrections possible. Testing was performed comparing different combinations of hole size and tool size on assemblies that contained external upsets to better determine the effects of SAG when using flex collars and stiffer slick collars with a constant diameter. Through empirical testing, it was determined that flex collars deflect in a manner which BHA modeling is unable to predict when external upsets are present. External upsets can include stabilizers, wear bands, worn BHA components, crossover subs, and reducing OD on BHA components. When modeling the flexure across the length of a flex collar or other complex BHA components, it results in a reduction in accuracy when predicting deflection of the BHA in the vicinity of survey instruments. Using collars with a constant diameter circumvents this issue; eliminating variations in diameter across the length of a component allows modeling software to predict the deflection, providing the most accurate results. The results of this study can provide the industry with a best practice to manage and reduce this effect until the modelling has advanced sufficiently to account for the SAG inaccuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".