MétaCan
Menu
Back to cohort
Record W2586721168 · doi:10.2118/185037-ms

EOR in Tight Reservoirs, Technical and Economical Feasibility

2017· article· en· W2586721168 on OpenAlexaboutno aff
Kevin Joslin, Shawkat Ghedan, Anson Abraham, Varun Pathak

Bibliographic record

VenueSPE Unconventional Resources Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringEnhanced oil recoveryOil in placeTight gasPermeability (electromagnetism)Tight oilFossil fuelGeologyEnvironmental sciencePetroleumHydraulic fracturingEngineeringWaste management

Abstract

fetched live from OpenAlex

Abstract Field experience indicates that primary depletion of tight oil formations, using multistage fractured horizontal wells, commonly recovers only 5 to 10% of OOIP. The impact of various EOR techniques on recovering additional oil from these formations is still not fully understood. This paper investigates the applicability of feasible EOR methods and determines their technical and economic success over the natural depletion process under different well and fracture designs. Additionally, the study investigates the minimum reservoir permeability required for success. To achieve the objectives, both black oil and compositional simulation models were generated for a Western Canadian tight reservoir containing volatile oil. In addition to primary, the EOR recovery processes that were considered include waterflooding, immiscible-N2 and miscible-CO2 gas flooding. Combinations of these techniques, coupled with the effects of various well and fracture design parameters were technically explored, and economically ranked using a comprehensive economic analysis. Furthermore, the optimal case of each process was subjected to sensitivity on matrix permeability to determine the minimum permeability at which these methods can be applicable. In the EOR scenarios evaluated, the highest cumulative oil produced was associated with the closest well and fracture spacing, and longest fracture half length. With a larger well spacing (in the order of 400 m), the wells were found to be too far apart to offer any benefit from any EOR technique. Additionally, the capital expenditure of tight-oil projects is high and therefore greatly influences the economic success. Several scenarios yielded similar NPV values, however, the IRR performances and CAPEX requirements helped further evaluate and rank the scenarios. For the reservoir model used, waterflood was found to be uneconomical at the initial permeability levels investigated (around 0.3 md) and required a minimum permeability threshold (1 mD) to become profitable. The primary recovery mechanisms in waterflooding are pressure maintenance and areal sweep, which were more pronounced in the N 2flood. This was the best recovery technique based on NPV. However, the best recovery technique based on oil recovery was the miscible-CO2 flood. It offered an increase in oil recovery factor from 11% to 23% over the best natural depletion case, which was a result of increased oil mobility by dissolution of CO2. At lower permeability values (down to 0.03 mD) immiscible-N2 flood became the most effective method via pressure maintenance within the drainage area. For even tighter reservoirs (under 0.03mD), natural depletion remained the best option for this reservoir. This paper provides an elaborate workflow for evaluating and optimizing EOR techniques in tight oil formations through an integrated modeling approach. It helps to identify the most technically and economically proficient techniques under different levels of permeability, well spacing and fracture parameters.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.049
GPT teacher head0.303
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueSPE Unconventional Resources ConferenceSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207