Laboratory Testing and Well Productivity Assessment of Drill-in-Fluid Systems in Order To Determine the Optimum Mud System for Alaskan Heavy-Oil Multilateral Field Developments
Bibliographic record
Abstract
Abstract The North Slope of Alaska has billions of barrels of heavy original oil in place (OOIP) residing in largely undeveloped reservoirs. Despite this large volume of heavy oil in place, the majority of reserves development on the slope to date has been focused on light crude. However over the past few years, operators have begun to develop the North Slope's vast heavy oil resource base. Recently, an optimum drill-in-fluid/formation damage minimization study was undertaken by ConocoPhillips, BP, and its partners in order to determine the optimum reservoir drilling fluid for the heavy oil sands in order to maximize well productivity and project value. This paper will provide an in-depth look at the drilling fluid design, laboratory testing, and productivity analysis associated with determining the optimum reservoir drill in fluid (RDF) for the West Sak, horizontal, multi-lateral, extended reach, field development. Lab test results to be reviewed will include: Crude Compatibility Lubricity (steel to steel & rock to steel) Shale Stability General Rheology Mud Filtrate invasion tests with core to determine Return Perm Filtercake Removal tests w/ceramic disk tests to determine return permeability and removal effectiveness With the lab test results, well productivity calculations will illustrate why an oil based mud (OBM) was determined to be the optimum drill-in-fluid for West Sak. Lab tests and inflow performance modeling show injectivity and productivity improvements associated with reducing water based filtrate invasion and permeability reduction due to water and ineffective filtercake removal. Results show that the overall process of integrating the drill-in-fluid, completion design, inflow performance modeling, and operations procedures should be considered a best practice and an example of how an integrated study ultimately provides a field development with the best project value. Using the current field development economic model, this process added reserves, improved project NPV & IRR and maximized project economics
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".