Bibliographic record
Abstract
Abstract An effective chemical treatment program should reduce the rate of well failures while enhancing well productivity and minimizing cost. The unique challenges to achieving these goals include the variation in the downhole conditions, fluid composition, completion types and number of wells. The execution of chemical programs often relies on third party vendors with a vast resource base and proven technology. These traits, however, must be coupled with knowledge of the well history and proper oversight from operations, facilities, and production engineering groups. Managing and effectively utilizing collected data helps to move from "blanket type" chemical programs to a more targeted well-by-well approach. This work uncovers several opportunities for improvement in already established chemical programs. It is especially beneficial for the onshore fields which are challenged with hundreds or even thousands of wells. The systematic improvement strategy applied in this study began by assessing existing data for identification of scaling, corrosion, and organic deposition problems. This allowed the Local Chemical Management Team (LCMT) to reveal gaps in the information needed for a more comprehensive understanding of formation damage and flow assurance issues. Proper identification of controlling damage type per formation and area of the field enabled redesigning of well completions, testing of water compatibilities for fracture stimulation, and customization of acid treatments with improved acid placement and production uplift. Early attempts to collect fluid samples and integrate water, gas and solid analysis in the scale prediction modeling software revealed the critical nature of quality checks in sampling procedures, field tests, and lab reports. The lab audits and chemical program data management led to reorganization of the database structure and improvement in measuring and reporting of the results to the LCMT. The value of information gained by laboratory tests offered by the chemical provider was assessed in current field settings and communicated to engineering and operations personnel. A better understanding of organic deposits also supplemented wellbore and sand face clean outs. Inclusion of a flow assurance focus in an established corrosion and well failure prevention program increased well productivity and decreased operating cost per well. Improvements in the database structure included utilization of historical data for treatment optimization, integration of oil, water, and gas with quantitative solids analysis, and the establishment of key performance metrics and reporting procedures. Customization of remedial treatments per zone and geographic location, in addition to a review of well histories and completions complemented production optimization practices. Awareness of flow assurance issues and chemical management programs was provided to operations and engineering staff through a number of on-site training sessions.
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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".