Selection of a Chemical EOR Strategy in a Heavy Oil Reservoir Using Laboratory Data and Reservoir Simulation
Bibliographic record
Abstract
Abstract Chemical enhanced oil recovery (CEOR) of heavy oils is growing in volume and scope due to advances in the technology and field experience. This work describes a new methodology to select a CEOR strategy in a heavy oil reservoir when several viable options exist. We applied this methodology to the Pelican Lake field in Alberta. We evaluated water flooding, polymer flooding, alkaline-surfactant-polymer (ASP) flooding, alkaline-co-solvent-polymer (ACP) flooding and polymer flooding followed by ASP flooding in laboratory tests. We executed new experiments including microemulsion phase behavior, polymer rheology and corefloods representing these various strategies. These experiments were designed to help understand the role of mobility control in chemical flooding of heavy oils. UTCHEM, the University of Texas Chemical Flooding Simulator, was used to model experimental results, and to scale them up in pilot simulations using heterogeneous geological models representative of Pelican Lake. We report results for the selection of promising CEOR strategies for implementation in Pelican Lake based on the new laboratory experiments, reservoir simulations and our qualitative understanding of their various advantages and disadvantages. We present simulation results of a pilot using horizontal wells in a heterogeneous geological model representative of the reservoir. We simulated the various chemical EOR processes using the matched experimental data and evaluated them in terms of total oil production, time to completion and complexity. In-situ oil viscosity and operational injection limits were evaluated as crucial sensitivities. We make recommendations for CEOR implementation based on simulation study results and our understanding of relative process risks and costs.
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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 | 0.001 | 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".