Optimization of Hydraulic Fracturing Design with Future EOR Considerations in Shale Oil Reservoirs
Bibliographic record
Abstract
Abstract Nowadays, shale oil (SO) resources gain much attention in the oil industry world-wide. SO is classified as an unconventional resource and an optimal hydraulic fracturing (HF) design with future EOR consideration is essential for achieving a high oil production rate. However, people are still faced with many issues in HF optimization and EOR implementation associated with a significant increase in capital costs in SO development. To overcome the current challenges, maximizing the oil recovery and minimizing the future investment costs, this paper presents an HF optimization and comprehensive evaluation of EOR potential in SO reservoirs in terms of maximizing oil recovery and project revenue. We first address the key importance for achieving a successful HF design. Then, different development strategies for improving oil recovery including waterflooding, continuous gas flooding, and cycling gas flooding are systematically evaluated. Finally, HF design and EOR gas flooding are optimized through a robust procedure. Four parameters that strongly affect the SO production have been identified: matrix permeability, fracture half-length, fracture spacing and rock compressibility. Detailed analyses of these key factors are addressed to allow the design of optimal and practical HF strategies to maximize the oil recovery. The analysis show that an EOR application is crucial for improving the oil recovery factor in SO. Different development strategies including natural depletion, waterflooding, and EOR gas flooding are implemented. Among them, continuous gas injection after 20 years' primary production is proven as the most promising method to improve oil recovery in both technical and economical points of views. The critical effect of a hydraulically fracturing pattern is examined in this study and it is shown that oil recovery in an aligned fracturing pattern yields a superior performance (a higher oil rate and slower pressure depletion) than the one in a staggered fracturing pattern. Moreover, the distance between an injector and a producer is investigated to obtain the highest oil production and the lowest injection cost. To maximize the oil recovery, a series of physics-based optimizations have been performed for HF design and EOR gas operation by applying the DECE algorithm in a robust optimizer. After the optimization process, the ultimate oil recovery yeilds about 13.32% for no injection and 18.58% for gas injection. The optimal values are also documented in this paper as a guideline for future HF and EOR gas flooding in SO. One of the important contributions of this research is to present a development strategy for SO reservoirs, in which HF optimization with future EOR considerations significantly helps to address future technical challenges, investment costs, and project economy. The proposed approach can be effectively optimized for HF design and served as a guideline for investigating the EOR potential in unconventional reservoirs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".