From Intelligent Injectors to Smart Flood Management: Realizing the Value of Intelligent Completion Technology in the Moderate Production Rate Industry Segment
Bibliographic record
Abstract
Abstract This paper is a case history which examines the successful application of Intelligent Completion (IC) technology in a cost sensitive, mature, onshore North American environment where an existing hydrocarbon miscible flood (HCMF) horizontal injection well was retrofitted to manage the injection support of two geologically distinct reservoir regions covering two well patterns. The value of IC technology is explored in this early deployment which saw a relatively low cost application targeted towards a mature asset. The beneficial results of this application of IC technology were measured in terms of well intervention cost savings and affected oil production. This paper presents a relative comparison of those benefits. Though this application of IC technology was originally justified by the avoidance of certain future well interventions to modify the injection profile, an analysis of the affected pattern production in the post-installation period showed that the benefit to the operator was appreciably more from enhanced reservoir management than from the cost savings which were associated with workover avoidance. Based on the success described in this case history, and reflecting upon the trends of intelligent well and smart field technology, the authors explore reasons for its relatively slow uptake in moderate production rate, brownfield applications. Large scale reservoir management of miscible flood projects using intelligent well and smart field technologies should provide significant value in terms of improved solvent/oil ratio through more efficient monitoring and management of the flood. This is probably the most compelling value proposition for IC technology application in moderate production rate land applications. This case history is intended to provide credible evidence of the benefit of IC technology in an application with cost challenges analogous to those faced by operators who are responsible for cost sensitive, moderate production assets. Secondly, it is intended to encourage the IC technology providers to develop more solutions for the brownfield segment of the industry, where profitability and value definition can be challenging.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".