Technical and Economical Screening of Chemical EOR Methods for the Offshore
Bibliographic record
Abstract
Abstract Enhanced oil recovery (EOR) and maximizing recovery from declining production fields remain a challenge in the offshore industry. The challenge is to find an EOR method that is both technically and economically feasible considering the high capital and operating costs in the offshore environment. The goal of this work was to conduct a simple cost-benefit analysis based on a technical, facility, and economical screening of chemical EOR methods applicable to the offshore. Offshore Newfoundland, Canada is used as a base case as it represents a challenging geographical environment. The reservoir properties are good, based on volumetrics and characteristics, but the fields are located over 300 km offshore in a harsh environment where operational costs are high. A data mining approach was used for the EOR screening process. Data from over one thousand core flooding experiments investigating various chemical EOR methods, including surfactant, polymer, alkaline-surfactant (AS), alkaline-surfactant-polymer (ASP), nanoparticle, and low salinity water injection (LSWI), were collected. Factors with the greatest influence on the performance of a given EOR method were statistically examined and discretized. The ranges of recovery factor, rock type, chemical concentrations, and the most commonly used chemicals are presented in this review paper. Economic factors examined included capital expenditures (CAPEX) and the operating cost of production (OPEX). Benefits are found to be strongly related to oil production and Brent crude oil forecasts. Sensitivity studies of the recovery factor ranges with the different chemical concentrations, net present values (NPV), and the influence of the inflation were all taken into consideration. Two different injection plans were considered: injection from day one of production, and injection after secondary production. The highest CAPEX and OPEX were calculated for the ASP method, whereas LSWI resulted in the lowest. The results indicate that most of the chemical EOR methods could be economically successful, however, the timing of implementation will affect the potential benefits. If high recovery and low chemical concentrations are considered, ASP flooding is the most successful chemical EOR method when injecting from day one. However, if the EOR method starts after a decline in production, surfactant flooding proves more beneficial, regardless of the scenario considered. This paper presents a systematic approach to chemical EOR screening, combining available technical data using a data analytics approach with economic and technical uncertainty.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".