Selection of a Chemical EOR Strategy in a Heavy Oil Reservoir Using Laboratory Data and Reservoir Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it