Performance Analysis of Autonomous Inflow Control Valve in a Heterogenous Reservoir Using CO2 Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract CO2 flooding is a proven method to mobilize the immobile oil in the reservoirs for enhanced oil recovery (EOR). Using CO2 for EOR has been commercially used for several decades in onshore and offshore oil fields in North America, Canada, and Brazil. The injection of CO2 will both improve oil recovery and contribute significantly to reduction of greenhouse gas emissions. Breakthrough and direct reproduction of CO2, and production of corrosive carbonated water are among the challenges with CO2 EOR projects. Breakthrough of CO2 leads to poor distribution of CO2 in the reservoir and low CO2 storage. Carbonated water production results in corrosion of process equipment on the platform. Autonomous inflow control valve (AICV) is capable of autonomously restricting the reproduction of CO2 from the zones with CO2 breakthrough, and at the same time produce oil from the other zones with high oil saturation. In addition, AICV can reduce the production of carbonated water. The objective of this paper is to investigate the impact of AICV on oil production in a heterogeneous reservoir where CO2 is injected for EOR. The AICV performance is simulated with a dynamic reservoir simulator in a CO2 EOR oil reservoir. AICV restricts the inflow of unwanted fluids such as pure water, gas, carbonated water, and pure CO2. To achieve the objective, experiments and simulations are conducted. Experiments are carried out with realistic reservoir fluids to generate single phase flow performance curves for AICV and for an orifice type inflow control device (ICD). Simulations are performed using CMG STARS, which is a multi-phase, multi-component reservoir simulator. The performance of AICV is evaluated and compared with perforated casing completion. The experimental results confirm the significant benefit of AICV regarding water and CO2 reduction compared to ICD. Under the same conditions and at a given differential pressure, AICV compared to ICD, reduces the water and CO2 volume flow rate by approximately 58% and 82%, respectively. Experimental AICV performance curves are used to generate the flow control device (FCD) tables in CMG STARS. The FCD tables are used to simulate the AICV behavior. The simulation results indicate that AICV reduces the water cut significantly. The cumulative water production is reduced by approximately 25% by using AICVs compared to the perforated casing completion. Indeed, reduction in carbonated water production will minimize the recirculation of CO2. Also, reduction in production of carbonated water will mitigate the problem related to the corrosion of the producing wells and process equipment on the platform. In addition, simulation results show that the AICV completion delivers the highest cumulative oil production after five years of production. From the environmental aspects, utilizing AICV in CO2 EOR projects will contribute significantly to reduction of greenhouse gas emissions. A better distribution of CO2 in the reservoir contributes to a larger storage capacity and thereby more CO2 storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".