EOR Field Experiences in Carbonate Reservoirs in the United States
Bibliographic record
Abstract
Abstract A considerable portion of the world's hydrocarbon endowment, and even more so if resources from the Middle East are excluded, are in carbonate reservoirs. Carbonate reservoirs usually exhibit low porosity and may be fractured. These two characteristics in addition to oil-to-mixed wet rock properties usually results in low recovery. When enhanced oil recovery (EOR) strategies are pursued, the injected fluids will likely flow is through the fracture network, bypassing oil in the rock matrix. The high permeability in the fracture network and its low equivalent porous volume result in early breakthrough of the injected fluids. Infill drilling programs and well conformance strategies, mostly gas and water shutoff, have been effectively used to mitigate the early breakthrough and increase oil recovery. However, in most cases, 40 to 50% of the original oil in place (OOIP) is not produced. A large number of EOR field projects in carbonate reservoirs have been reported in the literature since the early 70's. The field projects showed the technological capability to increase oil recovery and estimated long run costs for their operation. This increase in oil recovery would directly result in additional reserves extending the productive life of the different assets. However, the technical results were not matched by their economic viability given the price environment of the time. In some cases high upfront investments created insurmountable barriers for the technology's application despite the promise of higher returns. In other cases, the high marginal costs eliminated all benefits from the increased recovery. The latter was especially true for EOR processes based on chemical and thermal methods. Over the last three decades, many improvements have reduced the cost per incremental barrel as will be seen below. Carbon dioxide flooding (continuous or alternating with water-WAG) is the dominant EOR process in the United States, mostly due to the availability of appropriate CO2. CO2 EOR is also the stepping stone towards sequestering carbon which could become a future business opportunity if carbon trading ever is implemented. This paper presents an overview of EOR field experiences in carbonate reservoirs in the United States, an analysis of recent efforts and discusses briefly on new opportunities for novel chemical methods. The main EOR experiences reviewed are CO2 injection, polymer flooding, steam injection and in-situ combustion (air injection).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".