First Autonomous Inflow Control Valve AICV Well Completion Deployed in a Field Under an EOR Water & CO2 Injection Scheme
Bibliographic record
Abstract
Abstract Enhanced oil recovery (EOR) schemes utilizing CO2 and water injection often experience significant problems and challenges with short circuiting of CO2 gas and water between injectors and producers, thereby leaving significant oil behind. Presented herein is the description and results of a field trial of new downhole flow control technology designed to provide autonomous inflow control of produced fluids from each zone in multi-zone wells. The new technology deployed involves integrating an autonomous inflow control valve (AICV) and a conventional (passive, non-autonomous) inflow control device (ICD) into a unique 3-position "sliding sleeve", shifted by coil tubing, to allow performance comparison between the two different inflow control devices as well as multiple flow control settings of each type. The AICV was designed (and lab tested) to selectively choke back or shut off flow of free CO2 gas and also high watercut (>99%), thereby significantly improving reservoir sweep and yielding higher oil production. The AICV opens or closes autonomously depending on sensed properties of wellbore fluids. Prior to installing the advanced completion, the multi-zone (vertical) trial well was extensively characterized using PLT and other log data, which was then inputted into a commercially available computer model to help design the AICV and ICD settings. The field trial was designed to evaluate the use of flow control in the EOR scheme over a wide range of flow rates and also to compare the two different flow control technologies at different settings within the same wellbore and reservoir condititions. This paper presents the results of the world's first field trial of the AICV in a Water and CO2 injection scheme, and the world's first comparison with conventional ICD technology in the same well. In addition to lab tests, the early field trial results of the advanced flow control completion are compared with historical production and PLT data where the zones were comingled (without any downhole flow control). A performance comparison of AICV versus conventional ICD, along with conclusions, implications for other wells in the same field and other fields, and lessons learned, are all presented herein.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".